Indonesia's Financial Liberalization: An Empirical Analysis of 1981-88 Panel Data 9789814379151

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INDONESIA'S FINANCIAL LIBERALIZATION

The Institute of Southeast Asian Studies (!SEAS) was established as an autonomous organization in 1968. It is a regional research centre for scholars and other specialists concerned with modern Southeast Asia, particularly the many-faceted problems of stability and security, economic development, and political and social change. The Institute is governed by a twenty-two-member Board of Trustees comprising nominees from the Singapore Government, the National University of Singapore, the various Chambers of Commerce, and professional and civic organizations. A ten-man Executive Committee oversees day-to-day operations; it is chaired by the Director, the Institute's chief academic and administrative officer. The ASEAN Economic Research Unit is an integral part of the Institute, coming under the overall supervision of the Director, who is also the Chairperson of its Management Committee. The Unit was formed in 1979 in response to the need to deepen understanding of economic change and political developments in ASEAN. A Regional Advisory Committee, consisting of a senior economist from each of the ASEAN countries, guides the work of the Unit.

ISEAS Currrent Economic Affairs Series

INDONESIA'S FINANCIAL LIBERALIZATION An Empirical Analysis of 1981-88 Panel Data

Miranda S. Goeltom Faculty of Economics, University of Indonesia

liil...... 11!1!!111!

ASEAN Economic Research Unit INSTITUTE OF SOUTHEAST ASIAN STUDIES

Published by Institute of Southeast Asian Studies Heng Mui Keng Terrace Pasir Panjang Singapore 0511 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the Institute of Southeast Asian Studies. © 1995 Institute of Southeast Asian Studies, Singapore

The responsibility for facts and opinions in this publication rests exclusively with the author and her interpretations do not necessarily reflect the views or the policy of the Institute or its supporters.

Cataloguing in Publication Data Goeltom, Miranda S. Indonesia's financial liberalization: an empirical analysis of 1981-88 panel data (ISEAS current economic affairs series) 1. Finance--Indonesia. 2. Capital investments--Indonesia. I. Title. II. Series. HG187 I5S61 1995 sls94-53930 ISBN 981-3016-87-6 ISSN 0218-2114 Typeset by International Typesetters Printed in Singapore by Prime Packaging Industries Pte. Ltd.

For the light of my life WINDA AND ERMANDA

Contents

List of Tables Acknowledgements Macro-Economic and Policy Developments in Indonesia 1.1 Introduction 1.2 Macro-Economic Review and Economic Background to Deregulation 1.3 Analysis of the Impact of the Financial Reform on the Economy 1.4 Indonesian Manufacturing Sector and Financing Behaviour 2

3

4

Effects of the Financial Reform on Manufacturing Establishments 2.1 Introduction 2.2 Review of the Literature on Investment and Financial Constraints 2.3 Real and Financial Structure of Indonesian Manufacturing Establishments 2.4 Distribution of Debt and Production 2.5 Conclusions

ix xi

1 7 11

16 16 16 17 27 31

Econometric Evidence of the Effects of the Financial Reform on Capital Structure and Investment Choices 3.1 Introduction 3.2 The Basic Investment Model 3.3 Empirical Evidence 3.4 Conclusions

48

Efficiency and Credit Allocation 4.1 Introduction 4.2 Literature Overview and the Measurement of Efficiency

51 51 51

34 34

35 38

Contents

Vlll

5

4.3 Efficiency, Capital Structure, and Credit Allocation 4.4 Has the Allocation of Investment Improved After Liberalization? 4.5 Conclusions

69 74

Conclusions and Recommendations 5.1 Conclusions 5.2 Suggestions for Further Research

77 77 78

Appendices Bibliography The Author

53

80 89 95

List of Tables

1.1

1.2 1.3

1.4 1.5 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 4.5 4.6 4.7

Macro-Economic Indicators Nominal and Real Lending Rates Share of Credit Provided, by Type of Bank Effective Cost of Foreign Loans Small-Scale Credit and Permanent Working Capital Summary Statistics of Firms, by Size and Period Summary Statistics of Firms, by Organizational Form, Firm Size, and Period Summary Statistics of Firms, by Market Orientation, Firm Size, and Period Firms' Share of Debt and Value Added, by Firm Size and Period Firms' Share of Debt and Value Added, by Organizational Form, Firm Size, and Period Firms' Share of Debt and Value Added, by Market Orientation, Firm Size, and Period

2 9 9 10

12 20 24 26 28 29 31

Econometric Results of the Investment Equation Investment Equation, Allowing for Group Effects Investment Equation, Allowing for Market Effects Investment Equation, Adding Future Profit Effects as Explanatory Variables

42 44 46

Simple Correlation of Different Measures of Efficiency Testing the Restricted Model Efficiency, Risk, and Characteristics of Firms Correlation between Efficiency and Other Indicators Credit Allocation, Before and After Liberalization Domestic Credit Allocation, Before and After Liberalization Change in Credit Allocation between Pre- and PostLiberalization Periods

54 56 57 58 60 62

47

63

List of Tables

X

4.8 4.9 4.10 4.11 4.12 4.13

Change in Credit Allocation between Pre- and PostLiberalization Periods (Instrumental Variables Method) Time-Varying Determinants of Firms' Borrowing Time-Invariant Determinants of Firms' Borrowing Efficiency and Allocation of Investment Within Categories of Firms Efficiency and Allocation of Investment Across Categories of Firms Share of Investment and Profitability, by Firm Category

65 67

68 71 72 73

Acknowledgements

There are many individuals who assisted me and encouraged me to complete this work, starting from my years as a Ph.D. candidate at the Graduate School of the Department of Economics, Boston University, Massachusetts, USA. I am particularly indebted to Professor John R. Harris, my major adviser for the dissertation entitled "Financial Liberalization, Capital Structure and Investment: An Empirical Analysis of Indonesian Panel Data: 1981-1988". The same appreciation goes to Professor Fabio Schiantarelli, who gave me the inspiration to investigate this subject, and provided me with valuable advice. Special thanks also go to Professor Andrew Weirs for guiding me to other aspects of asymmetric information and efficiency, given his time constraint. Their genuine interest in my work definitely brought enlightenment and improvements. There is no way that I can fully express my gratitude and appreciation to them. My dissertation, which provides the basic background for this publication, could never have been done without financial assistance from the World Bank Education Project; Mr Marzuki Usman S.E., M.A., and Mr John Rahman S.E., who helped me with funding in the last stages of my study. I am also indebted to Professor Dr Arsyad Anwar, Dean of the Faculty of Economics of the University of Indonesia and Dr Darmin Nasution, head of LPEM-FEUI, who have given me the opportunity to study abroad; Dr Rustam Didong, Deputy Chairman of Bappenas, who allowed me to use this extremely rich data set; Mr Sugito and Mr Slamet Mukeno of the Central Bureau of Statistics, who helped in obtaining more information on the data. Many private and public enterprises have also provided invaluable help in obtaining data from their companies. My special thanks also to Lucy Zahner, the editor of my dissertation, Jack Chen of the Information Center of Boston University, and Dr Lisa Lynch for her understanding and concern of all my personal problems. To these individuals and organizations, I express my special thanks and deep appreciation. Special thanks to all my dear friends, Julia Watson, Monica Galizzi, Fidel Jaramillo, Jean-Yves Pitarakis, Gustavo Marquez, and Oloan P. Siahaan, who were always there to share the agony.

xii

Acknowledgements

It is very hard to express my gratitude to many of my family members and friends, who always cheered me from the sidelines. My mother has been encouraging and supportive all through the study, dissertation, and publication. Her constant prayers for the completion of my work have been very comforting. But there is no way that I can find the right expression to thank my daughters, Winda and Ermanda, and my husband Erwin. Their support, understanding, and patience during these years have made all the pressures become bearable. I am deeply indebted to them, and it is to them that I dedicate this book.

Macro-Economic and Policy Developments in Indonesia

1.1 Introduction

During the 1980s, Indonesia undertook a series of structural reforms in response to its deteriorating economic situation especially in the market for Indonesia's primary export, petroleum products. It was recognized that high economic growth could not be sustained by export earnings alone, and that diversification from oil-related export was needed. The deregulation of the banking system removed control on interest rates and administratively determined credit ceilings were abolished. The devaluation of the rupiah and trade deregulation were part of the programme to stimulate economic growth and improve resource mobilization. This chapter gives an overview of the Indonesian economy and discusses the process and effects of the financial liberalization. The reform in the financial sector should be examined in the context of the whole economic reform. Macro-economic and financial-sector indicators of 1981-88, which reflect the economic growth, capital accumulation, and financial deepening will be presented, as well as the behaviour of interest rates, and a brief review of the financial system in relation to the industrial policy. There is also an assessment on whether the dramatic switch in the regulatory regime generated any efficiency and growth benefits as predicted by financial repression literature (McKinnon 1973; Shaw 1973). 1.2 Macro-Economic Review and Economic Background to Deregulation

Since 1968 Indonesia has been having a high rate of growth, facilitated considerably by high oil prices in the 1970s and early 1980s. As an oil exporter, Indonesia experienced two major booms, in 1974-77 and 1979-82. The period of oil boom, which began with a quadrupling of oil prices in 1973 and continued with high prices until1982, had profound effects on the Indonesian economy. Table 1.1 shows the growth of its gross domestic product (GDP) since the beginning of 1970. During the first period of the oil boom (1972-81 ), the average annual change was 8.1 per cent, a very high figure compared with other

2

Indonesia's Financial Liberalization

developing countries. After 1983, the GDP growth rate rose steadily up to 1985, and increased thereafter to reach an annual growth of around 5 per cent. This high rate of growth was primarily due to the sharp increase in oil prices, which boosted the value of oil exports. By the end of 1981 the value of oil exports represented about 82 per cent of the total export value, as shown in Figure 1.1. The structure of the economy changed drastically after the oil shock in 1983, with the value of oil exports declining steadily, reaching a record low in 1981. The manufacturing sector instead increased its share of the total export, to take the place of the agricultural sector, which was traditionally the second biggest export-earning sector, reflecting the well-known "Dutch disease" syndrome. Macro-economic policy was fairly sound during this period, characterized by a concern to keep inflation under control and maintain a prudent fiscal policy. A cautious policy on foreign borrowing, following the infamous Pertamina affair of the mid-1970s, kept the country's debt service ratio fairly low throughout the boom period. For example, the ratio of public debt serviceto-exports was 9 per cent in 1981 and it only increased to 18 per cent in 1984. The government was actually less successful in controlling inflation, given its inability to sterilize oil revenues with its limited monetary instruments, and this resulted in an inflation rate of approximately 18 per cent by the end of 1982. At that time the economy was "overheated", with high levels of oil-related public investments and an upsurge in private investments, accompanied by more protectionist and interventionist policies. There was clear recognition that oil revenues were a temporary blessing, so the overall policy was directed towards channelling this money into investment in order to ensure sustained growth after the depletion of oil revenue. TABLE 1.1 Macro-Economic Indicators Annual Growth Rate(%) Year

GDP

Manufacturing

1972-81 1981 1982 1983 1984 1985 1986 1987 1988

8.12 8.26 4.78 7.82 6.03 2.26 5.95 4.76 5.72

1.56 1.22 2.20 19.23 18.99 8.28 5.50 10.60 12.96

NoTE: In constant 1983 prices. SouRcE: Calculated from Central Bureau of Statistics data.

1. Macro-Economic and Policy Developments in Indonesia

3

FIGURE 1.1 Indonesian Export (In percentages) 90 80 70 60 Q)

0>

.sc e

50

Q) Q)

40

a..

30 20 10

r-

r

r~= ~~~====r ~~~;:::::::

-

- ...::.:::.. '"" ......... ,.... ,.,.."·'

;.

0~------~----------~-----+------------------~

1981

1982

1983

1984

1985

1986

1987

1988

Year ............. Manufacturing

Oil

- - - . Agriculture

SouRcE: Central Bureau of Statistics (various issues).

Fostering growth in the manufacturing sector was a central goal of the government's policy. This was to be achieved primarily by channelling money to the private industrial sector through the banking system. The banks were instructed to finance at low interest rates certain types of investment, particularly in import substitution and backward integration of heavy industries, during the boom period. With the undeveloped capital market, the financial sector was typically repressed. Following sharp declines in oil revenues in late 1982, and again in 1986, policy-makers recognized the need for major reforms. First, non-oil exports had to be increased in order to maintain the flow of imports essential for continued development. Second, with the decline in oil revenues, fewer resources were now available to the public sector and therefore it became necessary to mobilize private savings. An integral part of the policy reform was the deregulation of the banking system in June 1983, which allowed banks to set interest rates; central bank liquidity credits were reduced substantially; and administratively determined credit ceilings were abolished. The general objective was to move away from administrative control to market allocation of credit flows. Falling oil prices in 1983, together with a world-wide recession and an increase in U.S. real interest rate, worsened Indonesia's balance of payments, thereby impairing its ability to service its debt. The government responded by

4

Indonesia's Financial Liberalization

devaluing the rupiah by 50 per cent at the end of March 1983, partly for budgetary reasons so that the nominal value of government revenues would continue to show an increase, but primarily in order to boost non-oil exports. Following this large discrete devaluation, the foreign exchange regime was changed to a crawling peg system in order to reduce volatile expectations of large dollar depreciations, which induced episodic large-scale capital outflows (Chant and Pangestu 1992). To reduce both external and internal imbalances, a series of austerity measures was also introduced, which included budget cuts, the postponement of some capital- and import-intensive projects, reduction of domestic fuel subsidies, and reduction of state enterprises and agricultural subsidies. The government also moved quickly in its efforts to increase the mobilization of domestic resources through reform in the financial sector, and by improved collection of non-oil tax revenues. This new policy immediately reversed the outflow of foreign exchange and prompted a recovery in 1984, as can be seen in Table 1.1. Prior to 1 June 1983, Indonesia had most of the characteristics of a financially repressed system. The banking sector was heavily regulated and entry was very much restricted. The market was dominated by state banks, with Bank Indonesia alone accounting for 35 per cent of the total assets of the entire financial system, and the five large state banks holding another 40 per cent. Bank Indonesia set ceilings on bank credits for individual banks. This was the principal means of control of monetary expansion because it was believed that reserve management alone was insufficient, given the volatility of international financial flows via oil revenues and the absence of restrictions on private capital movements. Over time, an extensive selective credit system with subsidized interest rates was introduced. Moreover, Bank Indonesia provided direct lending to some economic units, and channelled substantial amounts oflow-interest liquidity credits to high-priority or "strategic" sectors. These controls and credits provided the Indonesian Government with basic tools for channelling oil earnings to the private sector in order to increase investment. When oil revenues fell precipitously, the principal task facing the financial sector changed quickly to one of mobilization of domestic resources. Together with trade and industrial policies that were basically protectionist and primarily implemented through a detailed licensing system, this cheap credit policy created a few dominant economic groups, or "conglomerates", in the Indonesian economy, which prospered because of their ability to make use of the administrative allocation system (Robison 1986). In addition to privileged access to the domestic credit market, these groups were also able to make use of offshore loans because of their extensive links with financial and trading networks in Singapore, Malaysia, and Hong Kong. Entry restrictions via industrial licensing, receipt of quotas on imports and/or being granted monopoly importer status, and substantial interest rate reductions for credits to "priority sectors" created a number of additional distortions that generated profits for the

1. Macro-Economic and Policy Developments in Indonesia

5

firms (principally conglomerates) which were able to obtain privileged access to them. In addition, the majority of domestic, private banks in Indonesia have been acquired by these conglomerates, which have been able to use banks to gain access to credit for their non-financial operating units at prevailing deposit rates. Efforts to increase the mobilization of domestic funds through the financial sector and improve the collection of non-oil tax revenues were reflected in significant reforms in the 1983 banking deregulation and in the 1984 tax reform. The objectives of the banking deregulation are manifold. The deregulation was designed firstly to provide higher returns to depositors and lower costs to borrowers by raising the degree of competition in the financial market; secondly, to increase savings mobilization through the banking system; thirdly, to improve the efficiency of allocation of financial resources through increased reliance on the market mechanism; and fourthly, to increase the use of capital market instruments to raise equity capital and enhance the liquidity of shares. The measures taken included the abolition of credit ceilings, a reduction in liquidity credits, and the granting of permission for state banks to set their own interest rates on deposits and loans. Each of these measures required drastic changes in bank behaviour and in techniques of liquidity management. All banks were subjected to much greater competition and became responsible for acting on their independent assessments of profitable opportunities. Although the immediate effects of the 1983 banking reforms were to substantially increase interest rates paid on deposits and charged for loans, and to increase the share of GDP being channelled through the formal financial system, the anticipated changes in competitive behaviour emerged only slowly and were really given impetus with the later round of reforms in 1988 and 1989 (Chant and Pangestu 1992). Figure 1.2 portrays the evolution of key indicators in the monetary sector, M1/GDP and M2/GDP. It shows that there is an increase in the real size of the financial sector after the 1983 financial reform, as indicated by the increasing ratio of M2/GDP. In addition, the total volume of bank !endings had increased sharply during 1983-88, as shown in Figure 1.3. The banking deregulation was followed by a reform aimed at improving the collection of tax revenues from non-oil sources. This reform was undertaken in stages, beginning in 1984 with the abolition of withholding tax and the introduction of value-added tax. Subsequently, income and sales taxes were rationalized. These reforms were not followed by analogous changes in trade and industrial policies, which were not liberalized but became even more protectionist, so that by the beginning of 1985 more than 1, 100 products had been placed under import licence, import bans, or quotas. The worst drawback was that it further enhanced the dominant positions of major conglomerate groups, which had already benefited enormously from easy credit allocation policies. When oil prices fell further in 1986, the government was again forced to devalue the currency, and further deregulation measures were taken. The con-

FIGURE 1.2 Measures of Financial Development in Indonesia 35 30 0...

25

/

0

0

--- -- ---

20

Q)

Ol

ro

c

Q)

./

/

(!)

15

/ /

~

Q)

0...

10 5 0~----------------------------------------------~

1981

1982

1983

1984

1985

1986

1987

1988

Year Narrow money/GOP Broad money/GOP

Demand deposit/GOP Currency/GOP

FIGURE l.3 Volume of Bank Lending in Indonesia 50

40 .r:

ro

Q_

2

30

--

""0

c ro (/) ::J

0

20

--- --"" " /

.r: I-

10

····-···-

·········;/~-- ..........-:-..--;:··· O..··.c.:.:.:··.:..:.:;··.:..:..:..::··.:.::;··..:.:.:.:··.:.:::··.:::.:··:.:.:··.:_ - - - ../ ' - - 0~~~~~~~------------------------------~ 1981 1982 1984 1985 1986 1987 1988 1983 Year - - - - All banks ············· Private banks

State banks Foreign banks

SouRCE: Bank Indonesia, Indonesian Financial Statistics.

1. Macro-Economic and Policy Developments in Indonesia

7

tinuing change of policy towards increased market orientation reached its peak in 1988 in a series of major policy reforms, affecting primarily the banking system, capital markets, fiscal measures, and trade policy. These series of reforms have affected the real sector significantly (Chant and Pangestu 1992, pp. 1-5). The fall in oil prices from US$28 to US$9 per barrel forced the government to again carry out a devaluation of the currency by 45 per cent (from 1,134 to 1,644 rupiah per U.S. dollar) in September 1986. The plummeting oil and primary commodity prices shocked the government and induced it to accelerate the introduction of reforms. It moved promptly by implementing a series of tariff reforms such as removing most import licences; reorganizing customs, ports, and shipping operations; and introducing a duty-drawback scheme designed to provide internationally priced inputs to non-oil exporters. In 1987 a deregulation package was adopted to attract more foreign investment by removing various measures of discrimination vis-a-vis domestic investors and providing better and more attractive incentives for foreign investment. This new attitude towards free-market policies reached its peak when the government announced a package of banking and capital market deregulation in 1988. The essential elements of the new policies were the lowering of entry barriers and reduction of reserve requirements. Foreign banks were also allowed to open branches in other cities. The 1988 deregulation was very important and probably had more profound effects on the actual functioning of financial markets than did the 1983 measures.

1.3 Analysis of the Impact of the Financial Reform on the Economy In general, the financial reform was expected to increase the role of the market in the allocation of credit and consequently to reduce the role of government. This section examines the impact of the financial reform on the economy. It starts with a description of the scope of the reform and a discussion of its effects on the structure and competitive behaviour of domestic financial markets. Special attention will be paid to the emergence of conglomerates and their effects on domestic financial markets. The reform had an effect on transaction costs, as found in a study by Chant and Pangestu (1992), and this is also addressed in this section. Several measures of macro-economic indicators before and after liberalization are also analysed. Following the 1983 banking deregulation, interest rates on deposits at state banks almost doubled, to reach levels closer to those of private banks. For example, the average interest rate on six-month time deposits at state banks doubled from 6 per cent per annum in March 1983 to 11.5 per cent one month later, whereas private banks increased their deposit rate from 18.3 to 20.0 per cent. As a result, rupiah time deposits grew rapidly, by nearly 75 per cent in 1984 and by another 40 per cent in 1985, although the maturity structure

8

Indonesia's Financial Liberalization

became shorter (twelve months or less), Consequently, the banking industry had to adjust its lending rates, and with the increasing share of short-term fixedrate liabilities, banks took a more cautious stance in their credit policies. It was widely believed that the high cost of intermediation and the high credit risk of the financial system caused an unusually large spread between lending and deposit rates. Nasution (1986) calculates that, after deregulation, the weighted average cost of funds at state banks was in the range of 10-13 per cent and the prime rate was around 18 per cent. He concludes that inefficiencies of dominant state banks were also the main reason why competition after the deregulation had not lowered interest rates and narrowed real interest rate differential between domestic and international markets. It should also be noted that before the 1983 deregulation, the bulk of state bank credits carried an average nominal interest rate of less than 13 per cent (supported by low-interest liquidity credits from the central bank), whereas national and foreign private banks charged at least 21 per cent per annum, reflecting a significant segmentation in the credit markets (Woo and Nasution 1989). On the other hand, Chant and Pangestu ( 1992) suggest a somewhat different account. They argue that the Indonesian financial reforms ... succeeded in narrowing the margins between the interest revenues and interest costs of the Indonesian banks ... this evidence offers support for the view that financial reforms that eliminate administered interest rates and credit ceilings can improve the efficiency of the banking system.

Following the reform of 1983, particularly the relaxation of credit-allocation ceilings to individual banks, the share of loans provided by state banks fell, as did that of foreign banks, while the share of private domestic banks expanded. Such shares are given in Table 1.2. It seems that the financial reform has increased competition in the credit market, since the share of private banks (which are likely to be more professional and competitive) has increased dramatically. The financial reform has also raised real interest rates to a positive level. Table 1.3 shows the increase in nominal and real interest rates following the deregulation. The average nominal lending rate increased from about 9 per cent in 1982 to about 22.5 per cent in 1988, whereas associated real lending rates increased from -1.62 per cent in 1982 to 10.97 per cent by the end of 1988. Abrupt increases in these rates in 1984 and 1985 are particularly evident. It is obvious that, with the inflation rate remaining stable while nominal interest rates increased sharply after liberalization, real interest rates changed from negative to high positive rates very quickly . 1 It remains a puzzle how Indonesian real interest rates could remain so far below comparable rates in Singapore and Hong Kong given the absence of restrictions on private capital movements since 1967. Clearly, during the prederegulation period, Indonesian credit was a relative (absolute) bargain for those borrowers granted access to loans. Of course, that is another reason why

1. Macro-Economic and Policy Developments in Indonesia

9

TABLE 1.2 Nominal and Real Lending Rates (In per cent per year) Nominal Lending Rate

Inflation Rate

Real Lending Rate

Year

(i)

(n)

(r)

1981 1982 1983 1984 1985 1986 1987 1988

9.00 9.00 11.00 15.00 19.00 21.00 21.70 22.40

9.50 10.80 12.40 8.80 6.60 7.80 9.20 10.30

-0.46 -1.62 -0.36 5.70 11.69 12.83 12.08 10.97

SouRCE: Various issues of Bank Indonesia weekly report, and state and private banks annual report. TABLE 1.3 Share of Credit Provided, by Type of Bank Year

State Banks

Private Banks

Foreign Banks

1981 1982 1983 1984 1985 1986 1987 1988

0.87 0.87 0.85 0.81 0.78 0.75 0.75 0.72

0.08 0.08 0.10 0.13 0.17 0.20 0.21 0.24

0.05 0.05 0.05 0.06 0.05 0.05 0.04 0.04

NoTE: Figures reflect the lending to the private sector including state enterprises, but not including lending to the government. SouRcE: Bank Indonesia weekly report, 1980-89.

Bank Indonesia had to control the levels of lending under that regime since there must have been substantial excess demand. In the absence of such controls, one would expect interest rate parity to apply between Indonesian and offshore borrowing costs. Table l.4 converts the costs of borrowing U.S. dollars abroad into equivalent rupiah costs, taking into account depreciation ofthe rupiah against the U.S. dollar, which thereby increases rupiah cost of repayment. As far as foreign loans are concerned, the 1983 and 1986 devaluations resulted in a substantial

Indonesia's Financial Liberalization

10

increase in the effective cost of foreign loans, as shown in the final column of Table 1.4. These rates should be compared with the nominal rupiah rates (i) shown in column 2 of Table 1.3. In response to the 1983 reform, Indonesia's nominal interest rates have risen sharply over the period while international nominal rates have declined. In fact, the adjustment "overshot" in that Indonesian deposit and lending rates were higher than the levels that would have been dictated by interest rate parity. By 1988, adjusted foreign-borrowing rates were considerably lower than in Indonesia, and this trend accelerated after 1989. With the average interest on rupiah loans at nearly 22 per cent a year after 1984, established Indonesian firms could borrow at the Singapore or London inter-premium, which resulted in nominal loan rates ranging between 7.5 and 10 per cent, with a foreign exchange swap facility available at premiums of between 12 and 16 per cent. As far as exporters were concerned, it was cheaper to borrow offshore, even without the swap facility, because their dollardenominated export revenue could protect them from exchange-rate risk. It is evident that, through 1985 the relatively low nominal interest rates in Indonesia, combined with substantial levels of devaluation, made domestic borrowing attractive relative to borrowing from abroad. This changed in 1985, although the large devaluation in 1986 again temporarily altered the situation. However, by 1988 adjusted foreign-borrowing rates were considerably lower than in Indonesia, and this trend has been accelerating since 1989. Thus, an effect of the deregulation has been to increase the advantages that can be obtained by TABLE1.4 Effective Cost of Foreign Loans 6-Month Libor Rate

% Change in US$ Exchange Rate

Effective Cost of Foreign Loan(%)

Year

(iJ

(8)

(rJ

1981 1982 1983 1984 1985 1986 1987 1988 1989

16.72 13.60 9.93 11.29 8.64 6.25 7.93 9.43 8.31

0.28 4.68 41.48 7.74 5.42 46.4 0.03 4.0 4.48

17.00 18.28 51.41 19.03 14.06 52.65 7.96 13.43 12.79

NoTE: 8 is the ex-post exchange rate depreciation at the end of the calendar year, and was chosen due to the non-existence of the forward exchange rate market in Indonesia. Libor (London inter-bank offer rate) was chosen because it was extensively used as a benchmark in most foreign loan agreements.

1. Macro-Economic and Policy Developments in Indonesia

11

firms with access to offshore borrowing, which of course are primarily the conglomerate units and foreign firms. The advantage of borrowing from abroad is particularly clear for exporters whose revenue is in foreign exchange. This has raised a hotly debated issue in Indonesia as to whether the reforms that have increased interest rates have helped or disadvantaged smaller and nonconglomerate firms, which have less access to "cheap" offshore borrowing.

1.4 Indonesian Manufacturing Sector and Financing Behaviour Indonesian manufacturing has grown remarkably since the early 1970s, maintaining real growth rates of value added in excess of 12 per cent per annum. The best description of the changing structure of firms, by sector, size, and ownership is provided by Hill (August 1990, December 1990). Indonesian credit markets have been highly segmented, and different kinds of Indonesian firm~ have very different access to capital. The ability to obtain external funds in domestic credit markets differs between small and large firms, between Chinese and non-Chinese firms, between private and public enterprises, between firms that are affiliated or owned by a group and independent firms, and between export-oriented and domestic-oriented firms. Moreover, the lack of exchange-rate controls makes it possible for firms that have established good reputations and close connections with the outside world to borrow money offshore. Since Indonesia has adopted a flexible exchange-rate system, foreign exchange risk is an important consideration for those who want to make use of this opportunity, especially since U.S. dollar-denominated loans usually carry a significantly lower interest rate than domestic loans denominated in rupiah. Unlike developed countries, there are no organized future exchange-rate markets in Indonesia. Instead, the Central Bank does offer a swap facility and those who have access to offshore loans can hedge exchange-rate risk by paying a certain margin. When it was introduced, the swap facility was very limited, with a maximum term of six months, and privileged groups were granted the option to have the facility extended once or twice. Financial institutions were free to set the premium charged to their customers, but the demand kept increasing due to exchange-rate uncertainty. Beginning in October 1982, a margin of 2 per cent was set by the Central Bank, and by February 1983 the financial institution premium was between 5 and 6 per cent, while Bank Indonesia premium was between 4.25 and 4.75 per cent. With the average domestic interest rate on rupiah loans at near 22 per cent per year and the offshore rate at less than 10 per cent, the swap facility made offshore borrowing cheaper and so higher in demand. With Sibor (Singapore inter-bank offer rate) or Libor (London inter-bank offer rate) plus 0.5 to 2.0 per cent risk premium ranging from 7.5 to 10 per cent, the implied rupiah interest rate on foreign loans using the swap facility was 12 to 16 per cent. As far as exporters were concerned, even without the swap facility, borrowing offshore was cheaper

Indonesia's Financial Liberalization

12

because their dollar-denominated export revenue will protect them from exchange-rate risk. It is worth noting that after October 1988, limits on the swap facility were removed and its term was extended to three years at the most, but the margin was to be determined by the prevailing difference between Sibor and domestic rates, which made it no longer differentially attractive. It is obvious that there are significant differences across firms in their access to foreign loans. Basically, the foreign option is open to conglomerates, large Chinese firms with connections to Singapore and Hong Kong financial markets, to foreign firms, and to exporters with established overseas customer relationships. Access to domestic credit also differs across firms. Although there were special credit schemes for small-scale industries, kredit industri kecil (KIK, credit for small-scale industry) and kredit modal kerja permanen (KMKP, credit for permanent working capital), they represented only a very small part of the total subsidized credit (see Table 1.5). Indeed, the bulk of subsidized credit went to larger firms that had the political connections, influence, and special channels to the banks due to their long-time relationships, coupled with the ability to provide collateralizable assets. Relatively new (young) independent firms that had not built up their reputation and political connections faced highly constrained access to low-cost credit. Many large Chinese firms have close links with banks and financiers in Singapore and Hong Kong, which allows them to borrow at competitive market rates using "reputation" rather than assets as collateral. Firms owned by indigenous Indonesians (pribumi) generally lack access to such offshore credit, though many have received preferential terms from state-owned banks. There is insufficient data to quantify the relative share of ownership held by indigenous and Chinese private companies, but it is widely believed in Indonesia that TABLE 1.5 Small-Scale Credit and Permanent Working Capital (As a percentage of state banks' total credit)

Year

Small-Scale Credit

Permanent Working Capital Credit

Total Credit to Small Industries

1981 1982 1983 1984 1985 1986 1987 1988

0.05 0.04 0.03 0.03 0.02 0.02 0.01 0.02

0.09 0.09 0.07 0.07 0.06 0.05 0.04 0.04

0.14 0.13 0.10 0.10 0.08 0.07 0.05 0.06

SouRcE: Bank Indonesia weekly report (various issues).

1. Macro-Economic and Policy Developments in Indonesia

13

Chinese-owned private capital developed its dominance of the private sector during the period of controls and has been able to further capitalize on its established base under deregulation (Mackie and Sjahrir 1989; Soesastro and Drysdale 1990). Firms producing goods for export are also treated differently. Prior to the 1983 banking deregulation, there were generous schemes or export credits carrying highly subsidized interest rates, which were extended through 1989. In addition, exporting firms found it relatively easy to borrow either offshore in U.S. dollar loans or, in some cases, domestic U.S. dollar-denominated loans. Since their revenue was in U.S. dollars, they were relatively insulated from the risk of exchange-rate fluctuations. 2 Private firms differ from public enterprises with respect to access to domestic finance. Before 1983, public enterprises had ready access to funds, including a whole package of incentives such as increased government equity, subsidized interest rates on loans, as well as two-stage loans from foreign donors with a high grant component. 3 With regard to private firms, many Indonesian Chinese firms and some of the big Indonesian firms were affiliated with or belonged to groups that owned manufacturing firms, large trading companies, and banks at the same time (hereafter called "conglomerates"). Most interesting from the point of view of this study is the role these groups played in reducing the financial constraints of member firms. Establishments owned by a group usually received substantial loans on favourable terms from the bank owned by the group in addition to equity financing from the parent company as needed. Certainly this close relationship not only gave ready access to finance but also mitigated information and incentive problems that typically arose in the presence of asymmetric information. It is worth noting that most of the conglomerates in Indonesia belong to entrepreneurs of Chinese origin. Their rise to prominence in the economy was due to a complex variety of factors. One was (and is) the company's attractiveness to foreign manufacturers as a reliable and efficient domestic partner that has given rise to establishing numerous joint-venture subsidiaries. But corporate efficiency is not the only aspect affecting their attractiveness to foreign partners. In addition to satisfying local-participation requirements that were partly relaxed in 1986, potential local partners must first possess the capacity to obtain favourable arrangements such as sole-agency contracts for supply to the government or monopoly-importer licences. Political connections have been the first necessity in a situation in which access to licences and contracts are so important (Robison 1986). The second factor has been access to finance. As suggested above, most of the Chinese conglomerates were also able to gain access to networks of credit that extended from Hong Kong to Singapore among the overseas Chinese. However, one should not neglect the fact that once in a joint venture - typically with big Japanese firms, and later with

14

Indonesia's Financial Liberalization

Korean and Taiwanese firms- finance becomes a lesser problem. The magnitude of funds obtained in this way was well beyond the scope of underdeveloped domestic capital market, particularly in view of the credit ceiling placed on lending by domestic banks prior to the 1983 deregulation. The magnitude of unswapped foreign loans of this type was also far above that of the swap facility provided by the central bank. Small independent firms may find themselves rationed out in the formal credit market, like the one for commercial-bank long-term development loans. However, they may still have access to other sources of funds, such as suppliers' credit and formal "curb" markets, although those sources are more costly. Even in the informal markets there are significant differences. Small nonChinese firms are likely to face higher interest rates charged by money lenders in the curb markets, sometimes as high as 60 per cent per year. On the other hand, small Chinese firms are more able to borrow from their friends in Indonesia. The loans are known as bon-putih or literally "a piece of white notepad", and carry significantly lower rates than the other curb market rates. This specific type of loan is basically secured by reputation, since the Chinese business community is very tightly knit and there is great pressure to maintain one's reputation because of the severe punishment of boycott if a default occurs. Summarizing, there are profound differences among Indonesian firms in terms of their access to the credit market. The differences are not only in terms and interest rates, but also in different currencies, which have different exchangerate sensitivities. Some of them (in particular small, non-Chinese, independent, and young firms) are likely to face severe information problems and a lack of political connections. This limits their ability to obtain funds from the formal credit market (domestic or foreign) and forces them either to rely on internal finance or to raise funds from the informal market. Other firms, in particular those that belong to conglomerates, large Chinese firms, joint ventures with foreigners, and public enterprises are likely to have privileged access to the domestic credit market combined with the ability to borrow offshore. The differential access to, and cost of, external finance for different categories of firms is likely to have a profound effect on their investment choices.

NOTES 1. The nominal lending rate reported is the state bank average lending rate. The average lending rate of all Indonesian banks will push the rate slightly higher, but will not change the trend. 2. Although Indonesian banks accepted deposits denominated in U.S. dollars, they were reluctant to make domestic loans in U.S. dollars and generally held corresponding low-risk foreign assets. Their reluctance to lend in U.S. dollars against exporters' anticipated revenues is difficult to understand in relation to experience

1. Macro-Economic and Policy Developments in Indonesia

15

elsewhere with open capital accounts- Edwards and Edwards (1987) analyse the Chilean experience. However, there was considerable change after 1989 as banks became more aggressively competitive, although that is after the period being analysed in this chapter. 3. Two-step loans are extended by donors to the Government of Indonesia for the purpose of on-lending for specified purposes (for example, World Bank loans for small-scale industry credits). A number of such loans were specifically negotiated for the expansion of public enterprises.

g Effects of the Financial Reform on Manufacturing Establishments

2.1 Introduction The discussion in the previous chapter shows that Indonesian manufacturing establishments seem to have different access to credit according to their characteristics. Evidence also exists that the elimination of administered interest rates and credit ceilings has changed previously negative real interest rates to relatively high positive rates. Relying on recent theoretical and empirical studies on the link between financial market imperfections and real activity, this chapter will examine whether the deregulation of the banking system has resulted in any relaxation of financial constraints that firms face in their investment behaviour. In particular, this chapter will analyse whether different types of firms have different financing behaviour before and after the financial reform. In order to analyse cross-sectional variations in financing and investment behaviour before and after the financial reform, establishment data for the manufacturing sector that are currently available in suitable form for the period 1981-88 only will be used. The 1981-84 period will be referred to as the "prederegulation" period on the assumption that changes instituted late in 1983 had insufficient time to affect real investment decisions until well into 1984, while 1985-88 will be referred to as the "post -deregulation" period. This dichotomization suggests a once-for-all regime shift that considerably exaggerates the reality. Rather, there was a fairly continuous process of deregulation of various aspects of the economy after mid-1983. Furthermore, the response of economic agents to these reforms took place fairly gradually. Nevertheless, for our purposes, the 1983 reforms were extremely significant for increasing levels of real interest rates, and reducing credit controls on individual banks. Dominant state banks were forced to act more autonomously and to base their lending decisions more on commercial criteria than had been the case before the reform.

2.2 Review of the Literature on Investment and Financial Constraints Over the last decade, studies that link financial structure and investment

2. Effects of the Financial Reform on Manufacturing Establishments

17

behaviour have been put on firmer theoretical ground, borrowing heavily from the economics of information and incentives. The common theme in this area is that informational asymmetries may introduce inefficiencies in financial markets that make external funds more costly than internal funds and hence may affect the way corporations finance their investment. 1 In the literature on financial market inefficiencies, Jaffee and Russell (1976) and later, Stiglitz and Weiss (1981) illustrate how asymmetric information and uncertainty about borrower quality can induce rationing in the credit market, even in the presence of perfect credit markets. Myers and Majluf ( 1984) and Greenwald, Stiglitz, and Weiss (1984) show that firms may pass up profitable investment opportunities by refusing to issue shares, because managers have valuable inside information and act in the interest of old stockholders. As a result of this adverse selection problem, there is a tendency to rely on internal sources of funds, and to prefer debt to equity if external financing is required. Predictions made by Bernanke and Gertler ( 1989), Calomiris and Hubbard (1990), Gertler and Hubbard (1988), and Blinder and Stiglitz (1983) examine the endogenous interaction between financial structure and real activity, and conclude that with credit market imperfections, borrower's investment decisions will be "excessively sensitive" to current cash flow. While subsequent papers have shown that their conclusions are sensitive to the particular form of the possible financial contracts that is assumed, Gertler ( 1988) in his recent survey brilliantly identifies three important issues emerging from theoretical literature on capital market imperfections. First, investment depends positively on borrowers' net worth-to-liabilities ratio, a high ratio of which will reduce their cost of capital by eliminating lemon premiums of external finance. Second, asymmetric information implies that borrower investment decisions will be more sensitive to current cash flow. Third, the inherent informational problems will be more severe for new firms with no track records.

2.3 Real and Financial Structure of Indonesian Manufacturing Establishments This section will focus on the evolution of the real and financial characteristics of a panel of Indonesian manufacturing establishments. 2 Using firm-level data has several advantages. First, it avoids the problem of substantial serial correlation, which typically arises when using aggregate time-series data. Secondly, the substantial cross-sectional variation helps to provide more precise estimates of the parameters. And thirdly, inter-firm differences can be explored using time and cross-sectional variation in the data. The panel has been constructed by taking advantage of information from two main sources. The first source is the annual survey of manufacturing establishments conducted by the Central

18

Indonesia's Financial Liberalization

Bureau of Statistics since 1975, including financial data available only after 1981. The second source is the Census of Manufacturing Industry conducted in 1986, which contains a measure of the replacement value of the capital stock and a breakdown of sales between exports and sales in the domestic market, data not available from the annual surveys. After checking for consistency in the data throughout the whole sample period, leaving out establishments that have non-positive capital stock or value added, and omitting outliers, the final count is 2,970 establishments that had at least one year of positive investment during the 1981-88 period. Detailed description of the selection of establishments and on the methods used for the construction of variables is provided in Appendix I. Table 1 of Appendix I will also show the number of firms categorized by size, market, and group. A key summary of statistics will be calculated for the entire sample as well as for sub-samples selected according to size of firm, status (conglomerate and non-conglomerate), and market (export or domestic). The size sub-samples are obtained by classifying firms into three categories according to number of workers. The establishment is classified as small if the number of workers at the first year of observation is less than 100, medium if the number of workers is between 100 and 500, and large if the number of workers is greater than 500. 3 Furthermore, the establishments are also classified into conglomerates and nonconglomerates. Establishments that belong to a group of firms engaging in different types of activities are classified as conglomerates. Finally, the third categorization is based on whether there is direct export of output for 1985. In order to see the effect of the 1983 financial liberalization on individual establishment behaviour, the sample period is also divided into two subperiods: pre-liberalization (1981-84) and post-liberalization (1985-88). The year 1984 is chosen as a cut-off to allow for the 1983 liberalization to take effect. The abolition of credit ceilings, the curtailment of liquidity credits, and the elimination of most interest-rate controls are expected to have different effects on establishments depending upon their size, age, whether they are conglomerates, and the main market of their products. The key summary statistics for our sample of 2,970 establishments are given in Tables 2.1 through 2.3. These tables report measures of firm productivity, profitability, and financial structure for the entire sample as well as for subsamples categorized according to firm size, firm grouping (conglomerate or non-conglomerate), and market orientation (exporting or non-exporting). The measures are the ratio of gross investment-to-capital stock, IlK, which reflects expenditures for both replacement and expansion; the ratio of gross operating surplus-to-capital stock, P/K, which reflects total returns to capital independent of financial structure; the ratio of net operating surplus-to-equity, 4 S!Eq, which reflects the profitability of equity holdings; the ratio of stock of debt-tocapital stock, DIK, which captures the degree of leverage; and the ratio of value added-to-capital, VAJK, which is a measure of capital intensity. The ratio of

2. Effects of the Financial Reform on Manufacturing Establishments

19

interest payments-to-total debt (excluding trade debt), i/D, is used as a measure of the average cost of borrowed funds for each type of establishment. Variations in this ratio should reflect, in part, differential access to types of external finance. The discussion in Chapter 1 shows that indeed the deregulation of the banking system and the removal of interest-rate controls have substantially increased interest rates paid on deposits and charged for loans. Chapter 1 also describes how the manufacturing sector in Indonesia is deeply affected by the 1983 deregulation along with other structural reforms gradually implemented afterwards. One would expect that the abolition of credit ceilings, the curtailment of liquidity credits, and the elimination of most interest-rate controls will have different effects on establishments depending upon their differential access to external finance. How does this macro picture reflect in the sample of manufacturing firms? Although it is impossible to disentangle completely the effects of the financial reform from those of other factors influencing economic incentives and resource allocation in Indonesia during this period, the overall picture shows a pattern that is consistent with theoretical predictions of the impact of the financial reform. The results in Table 2.1 suggest that, overall, the financial reform appears to have increased the average cost of borrowing (as measured by interest payments over the stock of debt), from 16.9 to 20.6 per cent. At the same time, there is a substantial increase in capital intensity, VA!K, by more than 40 per cent from 0.34 to 0.51 per cent, while the investment rate, IlK, has risen by approximately 25 per cent, from 5.9 to 7.6 per cent. The high value of capital intensity is reflected in the profitability of investment (as measured by PIK), which has increased from 29 to 43 per cent. Despite the higher interest rates, the degree of financial leverage in the industrial sector has also increased. And increases in profitability in relation to increases in interest rates cause returns to equity (the ratio of operating surplus net of interest payments to capital stock minus debt) to rise even further, from gross levels of 42 to 76 per cent. These rates of return appear implausibly high. They are obviously biased upward significantly by several factors. First, taxes and depreciation are not subtracted from the numerator. Second, the value of working capital (inventories, accounts receivable, and holdings of financial assets) are not included in the denominator. However, our conclusions about the direction of change over time of rates of return, or about their relative values across different types of firms, are unlikely to be affected. Furthermore, it is clear from interviews conducted by J.R. Harris in 1988 and by the author in 1991 that Indonesian manufacturing firms expect high rates of return. Entrepreneurs stated that they considered only investment with payback periods of two to two-and-a-half years. For this constellation of outcomes to coexist in the aggregate, several

N 0

TABLE2.1 Summary Statistics of Firms, by Size and Period (In ratios)

Gross Physical Investment/ Capital Stock"

Operating Surplus Gross of Interest Payments"/ Capital Stock

Operating Surplus Net of Interest Payments"/ Capital Stock Minus Debt

Stock of Debt'/ Capital Stock"

Interest Payment/ Stock of Debt

Value Added/ Capital Stock"

Period

(IlK)

(PIK)

(S/Eq)

(D/K)

(i/D)

(VAIK)

Size of Firm

Number of Firms

Small

777

1981-84 1985-88

0.040 0.075

0.203 0.390

0.193 0.467

0.225 0.453

0.235 0.297

0.280 0.524

1,336

1981-84 1985-88

0.075 0.085

0.330 0.403

0.578 0.711

0.597 0.578

0.162 0.178

0.389 0.500

857

1981-84 1985-88

0.052 0.065

0.300 0.500

0.489 0.934

0.512 0.566

0.120 0.167

0.322 0.505

2,970

1981-84 1985-88

0.059 0.076

0.288 0.428

0.419 0.759

0.525 0.599

0.169 0.206

0.341 0.508

Medium Large

All firms

NoTE: An establishment is classified as small if the number of workers during the first year of observation is 20 to 99, medium-sized if the number of workers is I 00 to 500, and large if the number of workers is greater than 500. " Capital stock includes land, buildings, machinery. equipment, and other items. at replacement value. b Gross tax and depreciation. ' Not including trade credit. SouRcE: Author's calculations based on survey data. See Appendix I for details.

;;-

s-;:;

"'"'s· "'. ~

" ;:; 500 workers. Age 2 =Firms that start production between 1965 and 1974. Age 3 =Firms that start production before 1965.

Indonesia's Financial Liberalization

58

Table 4.4 shows the simple correlation coefficients between different measures of efficiency and three economic indicators, that is, profit per unit of capital (P/K), investment rate(///(), new debt (!1/K), and leverage ratio (TD/K). This relationship is allowed to be different for the pre- and post-liberalization periods. The results indicate that, indeed, profitability rate and new debt-tocapital ratio have a significant and positive correlation with technical efficiency, and that it is even higher after liberalization. The leverage ratio also shows a mixed story.lt is insignificant before liberalization, but turns out to be positive and significant after liberalization, except for NCRDS, whose association is instead smaller and significant. The investment rate also does not give a clear picture. Overall, there is an increasing association between investment and efficiency but it is not significant, except for NCRES, whose coefficients remain the same for both periods, but tum out to be significant after liberalization. To explore the matter further a regression method that allows for control of other factors is employed. How does efficiency affect the level of indebtedness of firms? What are other observable firm characteristics that can be used in explaining inter-firm variability in the degree ofleverage? A body ofliterature on the determinants of capital structure (Myers 1977; Tittman and Wessels 1988; and Mackie-Mason 1990; among others) shows that inter-firm variations in corporate indebtedness can be explained by variables that capture firm characteristics and financial market imperfections. Others who concentrate on agency cost problems (Grossman and Hart 1982, among others) have found that firms' degree of leverage is correlated with efficiency. Within this theoretical framework, Atiyas TABLE4.4 Correlation between Efficiency and Other Indicators CRES

P/K IlK fill/K TD/K

CRDS

NCRES

NCRDS

Pre

Post

Pre

Post

Pre

Post

Pre

Post

0.253 (0.03) -0.022 -(0.75) 0.013 (0.00) 0.048 (0.49)

0.307 (0.01) -0.049 -(0.04) 0.024 (0.01) 0.159 (0.02)

0.225 (0.00) -0.007 -(0.92) 0.009 (0.00) -0.012 -(0.85)

0.314 (0.01) 0.030 (0.01) 0.020 (0.01) 0.026 (0.00)

0.227 (0.00) 0.102 (0.14) 0.064 (0.01) 0.183 (0.01)

0.300 (0.00) 0.102 (0.1 0) 0.101 (0.03) 0.188 (0.00)

0.449 (0.00) -0.005 -(0.94) 0.019 (0.02) 0.088 (0.20)

0.503 (0.00) 0.022 (0.00) 0.014 (0.01) 0.067 (0.03)

-------"-·

NoTE: Marginal probability values are given within parentheses. Pre = Before liberalization, 1981-84. Post= After liberalization, 1985-88.

4. Efficiency and Credit Allocation

59

( 1991) provides an empirical analysis for the case of Colombia. He concludes that a firm's level of technical efficiency explains its level of indebtedness. Based on the above literature, this study explores the determinants of a firm's borrowing in the panel of manufacturing establishments, the most important variable here being technical efficiency. A more efficient firm will have a better chance to borrow more. Firm age and size may be good candidates to explain inter-firm variations in the degree of leverage, since smaller and younger firms have a shorter track record of past performance and are therefore more likely to be constrained in access to external financing. Another variable is the firm's profitability. A higher profit rate should increase internally generated funds that can be retained, and therefore relatively less debt is needed. The second characteristic that affects financial structure is risk. Is there an appropriate degree of leverage in relation to the degree of risk involved in a production process? Is the change in the level of leverage before and after liberalization related to the degree of risk involved in an investment? Different studies provide different answers. In a world of perfect capital markets where there is neither differential risk on investment nor informational asymmetry in the financial market, financial structure is irrelevant (Modigliani and Miller 1958). However, both the bankruptcy cost approach and the agency cost approach predict that firms with more variability in earnings should have a lower leverage. The argument is that high variability in earnings implies a higher probability of failure, and therefore makes a firm less attractive to lenders. The coefficient of variation of the annual ratio of profit-to-capital stock (before interest and taxes) for each individual firm is used as an indicator of the variability of earnings. A third set of characteristics is related to market segmentation. The sample provides information on ownership (conglomerate or non-conglomerate), market orientation (exporter or non-exporter), and type of firm (private or public enterprise). In the previous chapters these firms' time-invariant characteristics are found to have different effects on their financing behaviour, before and after liberalization. Table 4.5 reports the first set of regression results. The dependent variable is the average of new debt-to-capital stock (,1D/K) for each period. This variable is regressed on size, age, export, public enterprise, conglomerate, sector, risk (assumed constant over periods), and different measures of efficiency indices. The following have been used as a measure of efficiency: CRES in columns I and 2, NCRES in columns 3 and 4, CRDS in columns 5 and 6, and NCRDS in columns 7 and 8. With the exception of NCRDS, the coefficient of technical efficiency index has the expected sign but is insignificant before financial liberalization and highly positive and significant after liberalization, suggesting that more efficient firms carry a higher level of debt per unit of capital. The coefficient of the

TABLE4.5 Credit Allocation, Before and After Liberalization ----

New Debt-to-Capital Ratio, MJ/K ------- --·--·

Pre Constant

-0.037 -(0.07) -0.049 Cong1o-(0.29) merate -0.122 Size 2 -(0.88) Size 3 -0.181 -(1.10) -0.084 Age 2 -(0.73) Age 3 -0.081 -(0.47) Public -0.131 enterprise -(0.78) Export -0.099 -(0.76) Sector 31 0.585 (1.02) Sector 32 0.228 (0.42) Sector 33 0.199 (0.34) Sector 34 0.171 (0.32) Sector 35 0.540 (0.01) Sector 67 0.288 (0.52) Sector 38 0.311 (0.59) CVPK 0.002 ( 1.54) CRES 0.106 (0.98) NCRES

Post

Pre

Post

Pre

Post

Pre

Post

-0.011 -(0.03) -0.000 _(0.00) 0.054 (0.49) 0.111 (0.86) 0.004 (0.04) -0.007 -(0.05) -0.044 -(0.33) 0.048 (0.87) 0.148 (0.33) 0.147 (0.34) 0.129 (0.28) 0.087 (0.21) 0.130 (0.31) 0.111 (0.25) 0.132 (0.32) -0.036 -(1.97) 0.169 ( 1.82)

0.021 (0.04) 0.029 (0.18) -0.161 -(1.10) -0.247 -( 1.38) -0.079 -(0.68) -0.087 -(0.47) -0.149 -(0.88) -0.106 -(0.81) 0.636 (1.11) 0.230 (0.43) 0.227 (0.39) 0.167 (0.31) 0.530 (1.01) 0.306 (0.55) 0.342 (0.65) 0.002 ( 1.38)

0.004 (0.01) -0.002 -(0.01) 0.051 (0.44) 0.100 (0.71) 0.001 (0.01) -0.016 -(0.12) -0.049 -(0.36) 0.050 (0.48) 0.083 (0.18) 0.103 (0.24) 0.112 (0.24) 0.061 (0.15) 0.075 (0.18) 0.099 (0.22) 0.109 (0.26) -0.044 -(1.96)

-0.034 -(0.07) 0.048 (0.29) -0.117 -(0.85) -0.176 -( 1.08) -0.085 -(0.74) -0.084 -(0.49) -0.129 -(0.77) -0.097 -(0.75) 0.557 (0.98) 0.210 (0.39) 0.196 (0.33) 0.171 (0.32) 0.528 (0.99) 0.274 (0.49) 0.316 (0.60) 0.002 (1.57)

-0.009 -(0.02) -0.001 -(0.00) 0.057 (0.52) 0.114 (0.89) 0.003 (0.03) -0.092 -(0.07) -0.042 -(0.32) 0.049 (0.48) 0.128 (0.29) 0.133 (0.29) 0.126 (0.27) 0.086 (0.20) 0.120 (0.29) 0.101 (0.23) 0.134 (0.32) -0.040 -(1.88)

0.099 (0.19) 0.023 (0.13) -0.199 -(1.37) -0.304 -(1.70) -0.070 -(0.61) -0.102 -(0.61) -0.160 -(0.95) -0.090 -(0.70) 0.765 (1.36) 0.205 (0.39) 0.257 (0.44) 0.277 (0.52) 0.509 (0.99) 0.247 (0.45) 0.340 (0.66) 0.002 (1.69)

-0.011 -(0.03) 0.007 (0.05) 0.068 (0.59) 0.124 (0.87) -0.003 -(0.04) -0.025 -(0.19) -0.044 -(0.33) 0.055 (0.54) 0.009 (0.02) 0.068 (0.16) 0.085 (0.18) 0.046 (0.11) 0.037 (0.09) 0.079 (0.18) 0.075 (0.18) -0.004 -(0.69)

0.109 ( 1.19)

0.133 ( 1.96) 0.100 (1.54)

0.164 (2.77)

CRDS NCRDS R-square

0.161

0.230

0.163

0.187

0.245

0.206

0.139 -0.004 (1.92) -(0.07) 0.174 0.178

NoTE: CVPK is a measure of risk, calculated as the coefficient of variation of the ratio of profit- (before interest and taxes) to-capital stock. Pre = Before liberalization, 1981-84. Post = After liberalization, 1985-88. See Table 4.3 for the definition of Size 2. Size 3, Age 2, and Age 3. Sector 67 merges manufacturing sector !SIC (Intemational Standard of Industrial Classification) numbers 36 and 37.

4. Efficiency and Credit Allocation

61

risk measure shows an interesting result. Before liberalization it was close to zero and barely significant. However, the coefficient becomes negative and significant after liberalization, reflecting the change in attitudes of both borrowers and lenders to avoid risky projects as cost of debt increases. On the other hand, the findings do not suggest an important role for firms' characteristics whose coefficients are generally not significant. It is very interesting to see whether the results will change if total new debt raised from the domestic credit market is regressed on these characteristics. The argument to run this experiment is that domestic credit flow is determined by domestic banks, whereas the open capital market system maintained by Indonesia during the period of this study allows firms to shift borrowing between domestic and offshore sources. Table 4.6 presents the results of running the same regression as Table 4.5 but with the new domestic debt (L1Ddom) as the dependent variable. The overall picture is similar. The coefficients of efficiency are smaller in the domestic debt equation than in the total new debt equation, but both estimations are insignificant except for NCRDS. Even after liberalization, the coefficients of efficiency on domestic debt are increasing, but still smaller than those of the total new debt. However, for measures that are thought to be more adequate (CRDS), the coefficients for both sub-periods are highest and significant. Another interesting feature is that the coefficient of risk, which is positive and significant before liberalization, turns out to be insignificant after liberalization. Nevertheless, the moral of the story is the same, that efficiency becomes more significant after liberalization. One possible explanation for this similarity in the result of both total debt and domestic debt regression is that the share of domestic debt as a ratio of total debt in the sample is very high (0.783). The rest of this chapter will therefore concentrate on the investigation of allocation of total debt only. The results of the previous regression provide some support to the case of omitted variable bias. So, economic variables other than firms' time-invariant attributes are included in the regression in Table 4.7. The dependent variable is the difference between pre- and post-liberalization average new debt-tocapital ratio, that is, (L1D/K)ro>~- (L1D/K)rce· One problem with including these economic variables is the problem of endogeneity, where they might be highly correlated with error terms. Therefore, additional explanatory variables of the pre-liberalization period only are included. Those are the pre-liberalization average cash flow-to-capital ratio (LCFK), pre-liberalization leverage ratio (LTDK), and growth opportunities (YGRW) in the pre-liberalization period. Cases in which risk is either included or not included in the regression are presented. Table 4.7 presents eight equations, each differing in specifications of production function (CRES, NCRES, CRDS, NCRDS) and the inclusion or exclusion of the risk factor. As expected, a firm's size seems to influence its ability to raise new debt, though the statistics are not highly significant. The same conclusion can be derived for export orientation. Even though the coefficient is not very signifi-

Indonesia's Financial Liberalization

62

TABLE4.6 Domestic Credit Allocation, Before and After Liberalization New Domestic Debt-to-Capital Ratio, ,1Ddom/K Pre Constant

--0.028 -(0.06) Conglo--0.002 -(0.01) merate Size 2 --0.173 -(1.30) Size 3 --0.202 -(1.29) Age2 --0.060 -(0.54) Age 3 --0.055 -(0.34) Public --0.104 enterprise -(0.64) Export --0.044 -(0.35) Sector 31 0.380 (0.69) Sector 32 0.155 (0.30) Sector 33 0.140 (0.25) Sector 34 0.117 (0.23) Sector 35 0.380 (0.78) Sector 67 0.231 (0.43) Sector 38 0.237 (0.47) CVPK 0.002 (2.00) CRES 0.042 (0.40) NCRES

Post

Pre

Post

Pre

Post

Pre

Post

--0.026 -(0.06) --0.013 -(0.10) 0.097 (0.90) 0.110 (0.86) 0.001 (0.01) --0.005 -(0.04) --0.037 -(0.29) --0.004 -(0.04) 0.206 (0.46) 0.147 (0.35) 0.180 (0.40) 0.102 (0.25) 0.170 (0.41) 0.116 (0.27) 0.163 (0.40) --0.004 -(0.69) 0.087 (1.84)

--0.001 (0.00) 0.008 (0.05) --0.193 -(1.37) --0.235 -(1.37) --0.057 -(0.52) --0.053 -(0.32) --0.113 -(0.69) --0.048 -(0.38) 0.420 (0.76) 0.159 (0.28) 0.120 (0.24) 0.120 (0.24) 0.406 (0.80) 0.243 (0.46) 0.259 (0.51) 0.002 (2.03)

--0.005 (0.01) --0.016 -(0.12) 0.092 (0.81) 0.093 (0.67) --0.003 (0.04) --0.017 -(0.13) --0.044 -(0.34) -0.003 (0.03) 0.133 (0.30) 0.097 (0.23) 0.162 (0.35) 0.072 (0.17) 0.106 (0.26) 0.103 (0.24) 0.138 (0.34) --0.000 -(0.79)

--0.028 -(0.06) --0.002 (0.01) --0.171 -(1.29) --0.200 -(1.28) --0.061 -(0.55) --0.057 -(0.35) --0.103 -(0.64) --0.043 -(0.35) 0.366 (0.67) 0.147 (0.28) 0.138 (0.25) 0.116 (0.23) 0.393 (0.77) 0.226 (0.42) 0.238 (0.47) 0.002 (2.00)

-0.024 -(0.06) -0.014 -(0.10) 0.101 (0.95) 0.114 (0.90) --0.002 (0.02) -0.009 -(0.07) --0.036 -(0.27) --0.003 -(0.03) 0.179 (0.41) 0.130 (0.31) 0.176 (0.39) 0.100 (0.24) 0.157 (0.38) 0.104 (0.24) 0.166 (0.41) -0.000 -(0.97)

0.072 (0.15) --0.021 -(0.13) --0.238 -(1.70) --0.299 -(1.75) --0.046 -(0.42) --0.061 -(0.38) --0.125 -(0.77) --0.041 -(0.33) 0.585 (1.08) 0.179 (0.35) 0.205 (0.37) 0.217 (0.42) 0.425 (0.86) 0.218 (0.41) 0.289 (0.58) 0.002 (2.18)

-0.023 -(0.06) --0.005 (0.04) 0.114 (1.00) 0.123 (0.88) -0.009 -(0.10) -0.028 -(0.21) -0.038 -(0.29) 0.005 (0.05) 0.040 (0.09) 0.051 (0.12) 0.128 (0.28) 0.054 (0.13) 0.056 (0.14) 0.076 (0.18) 0.094 (0.23) -0.000 -(0.86)

0.051 (0.58)

0.054 (1.62) 0.100 (1.44)

0.120 (1.97) 0.099 (1.44) 0.161

0.007 (0.13) 0.159

CRDS NCRDS R-square

0.152

NoTE: See Table 4.5.

0.127

0.153

0.178

0.161

0.121

TABLE 4.7 Change in Credit Allocation between Pre- and Post-Liberalization Periods Change in Credit Allocation, (&J/K)Po"- (&J/K)P'" Eq. 1 Constant

Eq. 2

-0.066 -(0.11) Conglo-0.012 merate -(0.06) Size 2 0.265 (1.61) Size 3 0.280 (1.47) Age2 0.134 (1.03) Age3 -0.029 -(0.14) Public 0.155 enterprise (0.81) Export 0.211 (1.40) Sector 31 -0.190 -(0.27) Sector 32 -0.197 -(0.31) Sector 33 -0.304 -(0.45) Sector 34 -0.148 -(0.24) Sector 35 -0.534 -(0.85) Sector 67 -0.170 -(0.26) Sector 38 -0.240 -(0.39) 0.012 LCFK (1.82) -0.058 LTDK -(3.69) YGRW -0.322 -(1.61) CVPK CRES

0.024 (0.04) 0.000 (0.00) 0.310 (1.89) 0.305 (1.62) 0.095 (0.73) -0.091 -(0.46) 0.245 (1.26) 0.196 (1.32) -0.545 -(0.77) -0.329 -(0.52) -0.323 -(0.48) -0.247 -(0.40) -0.751 -(1.19) -0.202 -(0.32) -0.389 -(0.64) 0.117 (1.18) -0.087 -(3.98) -0.280 -(1.41) -0.003 -(2.21) 0.023 0.138 (2.47) (2.89)

NCRES

Eq. 3

Eq.4

Eq. 5

Eq. 6

Eq. 7

Eq. 8

-0.061 -(0.10) -0.013 -(0.07) 0.261 (1.52) 0.273 (0.13) 0.133 (1.03) -0.029 -(0.15) 0.153 (0.79) 0.210 (1.40) -0.207 -(0.30) -0.209 -(0.33) -0.308 -(0.45) -0.156 -(0.25) -0.549 -(0.89) -0.174 -(0.27) -0.246 -(0.40) 0.004 (1.05) -0.055 -(2.72) -0.321 -(1.61)

-0.027 -(0.05) 0.010 (0.05) 0.337 (1.94) 0.360 (1.69) 0.097 (0.75) -0.084 -(0.42) 0.259 ( 1.51) 0.201 (1.34) -0.489 -(0.70) -0.284 -(0.45) -0.322 -(0.48) -0.124 -(0.35) -0.681 -(1.11) -0.199 -(0.31) -0.375 -(0.61) 0.101 (1.08) -0.056 -(2.96) -0.284 -(1.43) -0.033 -(2.16)

-0.063 -(0.39) -0.015 -(0.08) 0.264 ( 1.61) 0.279 (1.47) 0.135 (1.04) -0.028 -(0.14) 0.157 (0.81) 0.210 (1.40) -0.158 -(0.23) -0.181 -(0.28) -0.293 -(0.43) -0.135 -(0.22) -0.512 -(0.81) -0.165 -(0.26) -0.220 -(0.36) -0.003 -(0.04) -0.080 -(3.70) -0.325 -(1.62)

-0.021 (0.04) -0.003 -(0.01) 0.303 (1.85) 0.298 (1.58) 0.097 (0.75) -0.083 -(0.14) 0.240 (1.23) 0.194 (1.30) -0.458 -(0.66) -0.282 -(0.45) -0.307 -(0.45) -0.230 -(0.37) -0.704 -(1.12) -0.177 -(0.28) -0.371 -(0.60) 0.106 (1.97) -0.067 -(3.56) -0.283 -(1.42) -0.025 -(2.13)

-0.142 -(0.24) 0.010 (0.05) 0.304 (1.77) 0.350 (1.66) 0.127 (0.98) -0.024 -(0.12) 0.167 (0.86) 0.212 (1.42) -0.450 (0.66) -0.284 -(0.46) -0.382 -(0.56) -0.253 -(0.40) -0.624 -(1.03) -0.195 -(0.30) -0.329 -(0.54) 0.023 (0.31) -0.062 -(2.76) -0.312 -(1.57)

-0.105 -(0.18) O.D18 (0.09) 0.374 (2.17) 0.423 (2.00) 0.090 (0.69) -0.063 -(0.32) 0.273 (1.39) 0.187 (1.26) -0.638 -(0.95) -0.275 -(0.45) -0.355 -(0.53) -0.325 -(0.52) -0.672 -(1.13) -0.151 -(0.24) -0.386 -(0.64) 0.110 (1.27) -0.085 -(3.99) -0.283 -(1.43) -0.027 -(2.37)

0.013 (1.14)

0.095 (2.15) 0.039 (1.68)

0.107 (1.90)

CRDS NCRDS R-square

0.528

NoTE: See Table 4.5.

0.540

0.527

0.539

0.528

0.539

-0.063 -0.129 -0.710 -(0.40) 0.542 0.529

64

Indonesia's Financial Liberalization

cant, the result confirms that exporting firms are more able to borrow than nonexporting firms. Contrary to what was found in numerous other studies, age has no significant effect on new debt In interpreting this result, it is worth noting that the age classification is somewhat arbitrary. Moreover, it is hard to rule out the possibility of measurement errors. Quite surprisingly, in explaining the allocation of credit, industry dummies are universally insignificant, contrary to the findings of Jaramillo (1992) in the Ecuadorian case. While there are a number of possible explanations for this result, one plausible explanation is found in Tybout ( 1983 ). He argues that an insignificant coefficient may suggest that credit has been allocated according to characteristics of borrowers without special consideration of the sector. All three economic variables have a significant impact on a firm's indebtedness. The cash-flow coefficient is positive though not highly significant, which might be interpreted as a sign of a firm's capacity to engage in profitable production, hence increasing its credibility. The leverage ratio is negative and highly significant, which is consistent with the agent-cost view that firms with high leverage ratios find it more difficult to borrow. Quite surprisingly, the coefficient of growth opportunities is negative though less significant. One possible explanation for this unexpected result is that a high rate of growth allows firms to rely more on internal financing and less on external debt With the exception ofNCRDS, all efficiency indices tum out to be positive and significant, and even more so if a risk measure is included in the regression. It is important to note that the coefficient of risk is negative and significantly different from zero, suggesting that higher risk will result in lower new debt For comparison and in order to take into account the potential endogeneity of the regressors, the same regressions are run by using Instrumental Variables, where efficiency is excluded from the list of instruments, and capital and efficiency units of labour are included to guarantee the identification of estimates. The results are presented in Table 4.8. Most estimates are similar to those in Table 4.7, except for a few exceptions. First, note that the coefficient of risk becomes insignificant Taken literally, this result suggests that risk has no role in the decision to lend nor in the ability to borrow. Second, the coefficient on cash flow has also become much smaller and insignificant This may suggest that there is a potential multicollinearity between cash f1ow and leverage ratio. But on the whole, and which is most important, efficiency indices are positive and increasing between the two policy regimes, and they remain significant in both periods. So far, sub-period average values of time-varying variables are used in all the regressions, which is not the most efficient way to estimate panel data. Therefore, the next exercise will explore a different way of estimating allocation of credit. The individual yearly debt -to-capital ratio will be used as the dependent variable, rather than the average of each period. One alternative way to get the correct estimates is to run a two-step estimation strategy, similar to the one conducted by Faini, Galli, and Giannini ( 1991) for the case of southern Italy.

TABLE4.8 Change in Credit Allocation between Pre- and Post-Liberalization Periods (Instrumental Variables Method) Change in Credit Allocation, (MJ/K)"""- (MJ/K)"" Eq. 1

Eq. 2

-0.048 (0.08) Conglo-0.029 merate -(0.14) Size 2 0.253 (1.43) Size 3 0.262 (1.22) Age 2 0.141 (1.04) -0.014 Age 3 -(0.06) Public 0.160 enterprise (0.82) 0.202 Export ( 1.27) 0.095 Sector 31 (0.06) -0.045 Sector 32 (0.04) Sector 33 -0.219 -(0.27) Sector 34 -0.059 -(0.08) -0.362 Sector 35 -(0.32) -0.104 Sector 67 -(0.14) -0.121 Sector 38 (0.0 1) LCFK -0.033 -(0.17) -0.583 LTDK -(1.41) -0.336 YGRW -(1.57) CVPK Constant

CRES

-0.020 (0.03) -0.049 -(0.23) 0.256 (1.40) 0.246 ( 1.18) 0.132 (0.93) -0.002 -(0.01) 0.209 (1.03) 0.182 ( 1.19) 0.343 (0.24) 0.114 (0.13) -0.106 -(0.14) -0.023 -(0.03) -0.215 -(0.22) 0.023 (0.03) 0.018 (0.02) 0.026 (0.12) -0.581 -(3.42) -0.337 -(1.55) -0.127 -(0.53) 0.144 0.245 (2.17) (2.44)

NCRES

Eq. 3

Eq. 4

Eq. 5

Eq. 6

Eq. 7

Eq. 8

-0.042 -(0.07) -0.022 -(0.10) 0.250 (1.31) 0.252 (0.98) 0.135 (1.04) -0.026 -(0.13) 0.148 (0.76) 0.207 ( 1.35) -0.130 -(0.15) -0.174 -(0.26) -0.283 -(0.40) -0.137 -(0.22) -0.513 -(0.78) -0.154 -(0.23) -0.211 -(0.32) 0.006 (0.06) -0.585 -(3.81) -0.325 -(1.61)

-0.054 -(0.09) -0.035 (0.17) 0.261 (1.35) 0.235 (0.93) 0.115 (0.87) -0.054 -(0.27) 0.216 (1.06) 0.185 (1.22) -0.057 -(0.07) -0.097 -(0.15) -0.196 -(0.28) -0.110 -(0.17) -0.480 -(0.73) -0.099 -(0.15) -0.180 -(0.27) 0.042 (0.37) -0.585 -(3.87) -0.311 -(1.54) -0.002 -(1.47)

-0.045 -(0.07) -0.032 -(0.14) 0.256 (1.49) 0.265 ( 1.27) 0.142 (1.04) -0.017 -(0.08) 0.163 (0.82) 0.203 (1.30) 0.087 (0.05) -0.055 -(0.06) -0.215 -(0.26) -0.050 -(0.06) -0.360 -(0.32) -0.117 -(0.17) -0.010 -(0.10) -0.036 -(0.17) -0.584 -(3.56) -0.339 -(1.54)

-0.028 (0.05) -0.012 -(0.06) 0.250 (1.38) 0.239 ( 1.15) 0.145 (0.99) -0.012 -(0.05) 0.208 (1.02) 0.181 (1.18) 0.536 (0.36) 0.196 (0.22) -0.043 -(0.06) 0.109 (0.14) -0.083 -(0.80) -0.011 -(0.02) -0.121 -(0.13) 0.067 (0.27) -0.582 -(3.39) -0.358 -(1.58) -0.009 -(0.36)

-0.039 (0.06) 0.039 (0.19) 0.210 (0.79) 0.182 (0.44) 0.142 (1.05) -0.039 -(0.19) 0.137 (0.68) 0.212 (1.41) 0.063 (0.05) -0.139 -(0.20) -0.227 -(0.30) -(0.034) -(0.04) -0.481 -(0.71) -0.167 -(0.25) -0.166 -(0.24) -0.018 -(0.16) -0.585 -(3.72) -0.329 -(1.62)

-0.183 (0.27) -0.070 -(0.35) 0.185 (0.74) 0.109 (0.29) 0.132 (0.95) -0.070 -(0.35) 0.182 (0.84) 0.197 (1.29) 0.294 (0.27) -0.037 -(0.06) -0.106 -(0.15) 0.069 (0.09) -0.414 -(0.63) -0.122 -(0.19) -0.092 -(0.14) 0.009 (0.07) -0.586 -(3.65) -0.323 -(1.57) -0.002 -(1.02)

0.040 (1.87)

0.060 (2.88) 0.149 (2.17)

0.349 (2.64) 0.096 (2.83) 0.004 0.521

0.163 (3.64) 0.010 0.519

CRDS NCRDS Sargan test R-square

2.614 0.526

0.870 0.525

0.214 0.528

-----

4.074 0.535

0.091 0.526

2.622 0.518

----

NoTE: For definitions of variables, see note to Table 4.5. The set of instruments are S2, S3, A2, A3, Conglomerate, Public enterprise, Export, LCFk, LTDK, GYRW, CVPRKAP, SECTOR, K, and EL.

66

Indonesia's Financial Liberalization

Up to this point, the average new debt equation has been estimated using different methods of estimation. It is equally interesting to investigate how the allocation of total debt changes between the two policy regimes. The two-step strategy will be used in the estimation of total debt allocation. In the first step, the debt-to-capital ratio is regressed on its time-varying determinants. All the measures will be standardized by capital stock, and to eliminate measurement error bias in the observed capital stock without eliminating sample information, the GMM is adopted, as discussed in Griliches and Hausman (1986) and Arellano and Bond (1988). The individual firm effect can then be computed (using the estimated coefficients) for each firm. Table 4.9 presents the results of the first step using the DPD package developed by Arellano and Bond (1988). It shows that, before liberalization, cash flow turns out to have no significant impact on the ability of firms to raise more debt. The larger positive impact of internal finance after liberalization can be associated with the idea that more internal finance is a sign of a more profitable firm, which therefore has a better chance of borrowing. The coefficient of the previous leverage ratio is negative before liberalization, though not significant. After liberalization it remains negative, but becomes much smaller and is still not significantly different from zero. These findings suggest that, even after liberalization, there is no significant change in the impact of past leverage ratios on the ability to borrow outside finance. In addition, contrary to what Atiyas (1991) and Jaramillo (1992) have found, the coefficient ofYGRW, which reflects the dynamic growth of output, seems to have no significant role on a firm's indebtedness. In the second step, the individual firm's effect recovered from regression in the first step is regressed on time-invariant determinants of borrowing. Table 4.10 shows the results of the second-stage regressions, using the standard Ordinary Least Squares (OLS) procedure. 6 For most variables, this approach yields results very similar to those obtained using average values of new debtto-capital stock. A striking difference compared with all previous results is in the switch of signs of the coefficients for efficiency between periods. The coefficient of efficiency is negative and significant before liberalization, but positive after liberalization. This suggests that, everything else being constant, efficiency is not an important factor in determining access to external finance in the pre-liberalization context of extensive directed credit system and subsidized interest rates. Hence, there is a possibility that more credit has been allocated to inefficient firms that suffer financial distress. But, as the credit market becomes more competitive after liberalization, only those who are efficient are able to borrow. Hill and Kalirajan (1991) have also found the same results for a panel of small textile industries in Indonesia. They conclude that, in addition to affecting the distribution of credit to inefficient firms, the provision of subsidized debt may be causing a reduction in managerial effort and a possible increase in X-inefficiency. After liberalization, more efficient firms carry a

4. Efficiency and Credit Allocation

67

TABLE4.9 Time-Varying Determinants of Firms' Borrowing

Independent Variable

Dependent Variable, Debt/Capital Pre

Post

0.074 (0.306)

-0.059 -(0.759)

CF!K

-0.012 -(1.357)

0.419 (1.699)

(D/K),_,

-0.481 -(1.009)

-0.066 -(1.086)

YGRW

-0.122 -(0.360)

-0.694 -(0.518)

Intercept

M,

-2.516 (218)

-3.215 (218)

M,

-0.215 (218)

-0.366 (218)

Sargan test

2.692 (4)

3.512 (6)

Wald test

0.469 (2)

0.496 (2)

NoTE: List of instruments: gmm (CF/K, 2, 2), gmm (D/K)t-1, 2, 2), gmm (IlK, 2, 2), gmm (oY/Y, 2, 2), and year dummies. Year dummies are not reported. t-statistics are given within parentheses. CF =Cash-flow net of interest payments. D/K =Total debt-to-capital ratio. YGRW =Output-to-capital growth. M 1 =Test for first-order serial correlation, distributed n(O, 1). M, =Test for second-order serial correlation, distributed n(O, 1). Sargan test, for over-identifying restrictions, distributed x' (pi' Wald test= Test of joint significance of financial variables. Pre= Before liberalization, 1981-84. Post= After liberalization, 1985-88.

higher level of debt per unit of capital. It is worth noting the coefficients of exporters, which are positive and significant, and even higher in the post-liberalization period. This may suggest that after liberalization relatively more credit has been allocated to exporting firms than non-exporting firms. Finally, the coefficients of public enterprise are

TABLE4.l0 Time-Invariant Determinants of Firms' Borrowing

Independent Variable

Dependent Variable, Individual Firm's Effects from Table 4.9* Pre

-0.391 -(0.36) Conglo0.048 (0.13) merate Size 2 0.358 (1.23) Size 3 0.023 (0.06) Age2 -0.295 -(1.21) -0.431 Age 3 -(1.18) Public 0.045 enterprise (0.12) Export 0.531 (1.92) -0.664 Sector 31 -(0.54) Sector 32 -0.779 -(0.67) Sector 33 -0.949 -(0.76) Sector 34 -0.372 -(0.64) -0.889 Sector 35 -(0.78) Sector 67 -0.025 -(0.02) Sector 38 -0.382 (0.34) CVPK 0.000 (0.30) -0.451 CRES -( 1.98) NCRES Constant

CRDS NCRDS

Post

Pre

Post

Pre

Post

Pre

Post

0.168 (0.12) -0.325 -(0.69) -0.342 -(0.91) -0.267 -(0.59) 0.538 (1.70) 0.375 (0.79) -0.465 -( 1.60) 0.538 (2.1 0) 1.299 (0.82) 0.400 (0.27) 1.128 (0.70) 2.50 (0.17) 0.789 (0.53) 0.402 (0.26) 0.986 (0.68) -0.000 -(0.10) 0.379 (1.88)

-0.514 -(0.47) 0.073 (0.20) 0.417 (1.35) 0.151 (0.39) -0.290 -(1.81) -0.388 -(1.06) 0.096 (0.27) 0.527 ( 1.89) -0.380 -(0.31) -0.558 -(0.48) -0.884 -(0.70) -0.593 -(0.52) -0.606 -(0.54) 0.025 (0.02) -0.291 -(0.26) -0.000 -(0.24)

0.342 (0.24) -0.380 -(0.80) -0.456 -(1.15) -0.467 -(0.94) 0.550 (1.73) 0.366 (0.78) -0.524 -(2.12) 0.531 (2.06) 1.358 (0.85) 0.349 (0.23) 1.182 (0.73) 0.206 (0.14) 0.695 (0.48) 0.433 (0.28) 1.042 (0.72) -0.000 -(0.11)

-0.400 -(0.37) 0.052 (0.14) 0.344 (1.19) 0.009 (0.03) -0.292 -(1.20) -0.421 -(1.15) 0.040 (0.11) 0.525 (1.90) -0.562 -(0.47) -0.710 -(0.61) -0.940 -(0.75) -0.736 -(0.64) -0.849 -(0.75) 0.032 (0.03) -0.413 -(0.37) -0.000 -(0.28)

0.174 (0.12) -0.327 -(0.70) -0.328 (0.87) -0.254 -(0.56) 0.535 (1.69) 0.364 (0.77) -0.462 -(1.99) 0.543 (2.12) 1.192 (0.76) 0.329 (0.22) 1.112 (0.69) 0.245 (0.17) 0.741 (0.50) 0.349 (0.23) 1.001 (0.69) -0.000 -(0.11)

-0.473 -(0.43) 0.026 (0.07) 0.346 (1.12) 0.054 (0.14) -0.272 -(1.11) -0.326 -(0.90) 0.080 (0.22) 0.485 (1.74) -0.024 -(0.02) -0.330 -(0.29) -0.740 -(0.59) -0.574 -(0.50) -0.367 -(0.33) 0.184 (0.15) -0.085 -(0.08) -0.000 -(0.15)

0.266 (0.19) -0.314 -(0.67) -0.350 -(0.88) -0.320 (0.64) 0.524 (1.64) 0.287 (0.61) -0.499 -(1.07) 0.575 (2.21) 0.838 (0.53) 0.042 (0.03) 0.978 (0.60) 0.151 (0.10) 0.373 (0.26) 0.229 (0.15) 0.760 (0.53) -0.000 -(0.20)

-0.269 -(1.68)

0.340 (1.75) -0.435 -(1.93)

0.354 ( 1.81) -0.116 -(0.75)

0.123 (0.62)

NoTE: See Table 4.5. *The dependent variable is the firm's specific residual obtained from the equation preand post-liberalization in Table 4.9.

4. Efficiency and Credit Allocation

69

positive before liberalization, though not significant, but negative and rather significant after liberalization. This is consistent with the fact that public enterprises have obtained less credit after 1983 as a result of the decline in government revenue from oil exports. The rescheduling, or even cancelling, of many mega projects owned by public enterprises as part of the austerity measures taken by the government, together with the removal of many types of subsidized or priority credits, is reflected in the negative sign.

4.4 Has the Allocation of Investment Improved After Liberalization? Another interesting issue is whether there was an overall improvement in the allocation of investment between the two policy regimes. In other words, is a higher proportion of investment carried out by better firms? And if it is, how does this differ, within groups of firms or across different groups, before and after liberalization? One simple way of examining this relationship is by generating less structured measures of efficiency of investment. This measure can be obtained by calculating the ratio of the actual returns, relative to the rate that would have been achieved if investment were distributed proportionately to the stock of capital of each firm. The argument is that if relatively more investment is undertaken by the more efficient firms, the rate will rise. This section provides three summary measures of whether post-liberalization investment resources have been more efficiently used. The idea is to calculate the ratio of total yearly returns on actual investment to the average total yearly returns on investment that would have been realized if the investment were weighted according to the firm's share in the capital stock. This ratio will be averaged over the pre-liberalization period and over the post-liberalization period, and the two averages will be compared. As a measure of returns on investment, two ratios can be used: ratio of profit- (before interest and tax) tocapital stock (measure A below), or ratio of value added-to-capital stock (measure B). The simple formulae to calculate them are as follows: (equation 7): T N

2: 2: lit * TT)Kit t

I

A= - - - - - - - - T

N

2: 2:lt * K)Kt t

* 7TiJKit

I

T N

2: 2: lit t

* VA,JK,t

i

B= T N

2: 2:lt * K)Kt

* VA,JKit

70

Indonesia's Financial Liberalization

where I;, is the total real investment of firm i at timet, 1T;, is the net profit before interest and taxes of firm i at time t, K;, is the real capital stock of firm i at time t, VA;, is value added of firm i at time t, K, is total real capital of all firms in year t, and I, is total real investment of all firms in year t. The data set used is the same as that of Chapter 2, consisting of 2,970 firms. The overall picture of the measure of profitability across the two periods is reported in the first row of Table 4.11. Both measures, A and B, suggest that indeed there is an improvement of the allocation of investment towards more profitable firms or class of firms after financial deregulation. It increases by 9.03 per cent if the profit-to-capital ratio, 1T;,/K,, is used as a proxy for rates of returns, and increases slightly more (13.6 per cent) if the ratio of value addedto-capital is used instead. Note that the results did not give any hint of the impact on different types of firms. In particular, it will be interesting to investigate whether the allocation of investment goes towards more efficient firms within the same category after liberalization, or whether it goes towards better and more efficient categories of firms. In calculating the effect of efficiency on the allocation of investment within the same category of firms, the sample is first divided into different categories, that is, size, market, group, public enterprise, and nine two-digit manufacturing sectors. Then calculations are done for each category and each period separately, using both profit-to-capital ratio (measure A) or value added-to-capital ratio (measure B) as a measure of returns to investment (equation 7). The results are also reported if each category is further subdivided into export and nonexport firms. The first column of Table 4.11 shows that there are significant shifts of investment towards more efficient firms within non-conglomerate firms, conglomerates, and sector 31 (food, beverages, and tobacco products), sector 32 (textiles, yams, and garments), sector 35 (basic chemicals), and sector 36 (nonmetallic mineral products). On the other hand, measures for sector 34 (paper products), sector 38 (machineries), sector 39 (others), and, to a lesser extent, sector 37 (basic metals) are smaller after liberalization, suggesting that the allocation of investment does not necessarily go towards more efficient firms within these categories. To gain a better insight into the result, the sample is further subdivided by export and non-export activities for all different categories. It is very obvious that export is the factor that drives the result. For example, the overall picture of small firms shows an indication that investment is allocated towards more profitable firms. But columns 2, 3, 5, and 6 of Table 4.11 prove that this is true only for exporting small firms, whereas measures A and B for non-exporting small firms have decreased instead. That is, for non-exporting firms investment is not allocated towards better firms. The same results are obtained if conglomerates are divided between exporting and non-exporting firms. Even for firms

TABLE4.11 Efficiency and Allocation of Investment Within Categories of Firms A Category

B

Period

All

Non-Export

Export

All

Non-Export

Export

Pre Post

1.229 1.340

1.414 1.299

1.151 1.403

1.199 1.362

1.371 1.296

1.156 1.406

Small

Pre Post

1.118 1.172

1.319 1.265

0.984 1.098

1.322 1.150

1.375 1.149

1.012 1.111

Medium

Pre Post

1.367 1.467

1.463 1.510

1.241 1.305

1.354 1.438

1.570 1.441

0.868 1.365

Large

Pre Post

1.218 1.264

1.288 0.933

1.233 1.426

1.220 1.364

1.250 1.053

1.225 1.527

Pre Post Pre Post

1.182 1.359 1.237 1.409

1.392 1.122 1.410 1.304

0.771 2.030 1.160 1.502

1.226 1.411 1.238 1.474

1.402 1.169 1.410 1.297

0.841 2.068 1.161 1.606

Sector 31

Pre Post

1.097 1.262

1.562 1.522

0.909 1.079

1.100 1.316

1.445 1.481

0.917 1.191

Sector 32

Pre Post

1.429 1.651

0.905 1.032

1.468 1.657

1.364 1.777

0.937 1.172

1.428 1.661

Sector 33

Pre Post

0.821 0.897

1.060 1.125

0.829 0.858

0.844 0.972

1.128 1.177

0.832 0.932

Sector 34

Pre Post

1.536 1.024

1.525 1.173

0.983

1.519 0.985

1.518 1.129

0.973

Sector 35

Pre Post

1.457 1.520

1.331 2.287

1.319 1.306

1.532 1.514

1.389 2.043

1.396 1.329

Sector 36

Pre Post

1.055 2.655

1.147 1.303

1.071 2.075

1.055 2.2294

1.139 1.317

1.074 2.217

Sector 37

Pre Post

0.860 0.842

0.993 0.787

0.851

0.823 0.807

0.940 0.719

0.822

Sector 38

Pre Post

1.531 0.901

1.622 0.893

1.235 0.968

1.492 1.058

1.572 1.055

1.223 1.061

Sector 39

Pre Post

1.683 1.604

1.840 1.659

2.144 1.048

1.526 1.635

1.699 1.5918

1.647 1.089

All

Size

Group

Conglomerate Nonconglo Sectors

Pre = Before liberalization, 1981-84. Post= After liberalization, 1985-88.

Indonesia's Financial Liberalization

72

that belong to a conglomerate, if they are not exporting, both measures have not gone down from the previous period. Moreover, differentiating between exporting and non-exporting firms for each class of industrial sector gives a very interesting story. Except for sector 35 (basic chemicals, coal, rubber and plastic products, that traditionally are not exported), the increase in the overall measure of efficiency is driven mainly by firms that export. The discussion above suggests that there has been a reallocation of investment towards more efficient and better firms in most categories. Whether or not this is evidence of reallocation towards better categories is dealt with in the next exercise. The following experiment is an attempt to investigate whether investment is allocated towards better categories of firms. For that purpose, an exercise is conducted taking one firm within a sub-category (such as small, medium, large for size category; or conglomerate and non-conglomerate for group category) as representative of that category. In other words, the data set is treated as if it consists of one small, one medium, and one large firm in the size category, or alternatively, as if there is only one conglomerate and one non-conglomerate firm in the group category of the sample. The same equation 7 is then used to derive the results displayed in Table 4.12. Table 4.12 portrays the fact that financial liberalization has helped to allocate investment towards more profitable sizes, markets, groups, and sectors. The increases between the pre- and post-liberalization periods are particularly significant across markets and sectors, with a smaller increase across groups. What is puzzling is the very small increase across size categories. How can these findings be explained? For this purpose, Table 4.13 provides a summary of the share of investment to total investment, Ijl,, and the ratio of TABLE4.12 Efficiency and Allocation of Investment Across Categories of Firms Firm Characteristic

Period

A

B

Size

Pre Post

0.9926 1.0567

1.0199 1.0909

Market

Pre Post

0.9821 1.2877

0.9957 1.0530

Group

Pre Post

1.0621 1.1888

1.0014 1.0093

Sector

Pre Post

1.0124 1.2529

0.8892 1.3237

Pre= Before liberalization, 1981-84. Post= After liberalization, 1985-88.

4. Efficiency and Credit Allocation

73

profit-to-capital stock, 1rJK,, across firm categories. Table 4.13 shows that after liberalization the profitability rate, as well as the share of investment, has almost doubled for small firms, with a much smaller increase for medium-sized firms. Also, large firms have decreased their share of investment a little, despite the fact that their profitability rates have increased dramatically. Since the group of large firms has the largest share of capital stock, the high profit rate is dominating the figure, leading to a very small increase in the measures across size. 7 The reallocation of investment across market categories goes towards the export sector. This is supported by the fact that more investment is carried out by export firms that experience a sharp increase in their profit rate, whereas non-export firms reduce their investment even though there is a slight increase in their profit rate. A similar picture is obtained if firms are divided between conglomerate and non-conglomerate status. TABLE4.13 Share oflnvestment and Profitability, by Firm Category

rc,JK,

!)I,

Pre

Post

Pre

Post

Size Small Medium Large

0.0361 0.2647 0.6992

0.0689 0.2954 0.6356

0.2034 0.3309 0.2999

0.3900 0.4033 0.5002

Group Conglomerate Non-congl

0.1349 0.8651

0.1919 0.8081

0.4595 0.3002

0.5813 0.3527

Market Export Non-export

0.4595 0.5405

0.5160 0.4840

0.3215 0.2485

0.4902 0.3028

Sector Sector 31 Sector 32 Sector 33 Sector 34 Sector 35 Sector 36 Sector 37 Sector 38 Sector 39

0.1950 0.1480 0.1183 0.0311 0.1128 0.1246 0.0524 0.2157 0.0021

0.2516 0.2232 0.0978 0.0252 0.1877 0.0431 0.0291 0.2386 0.0037

0.3726 0.1773 0.2133 0.1660 0.2976 0.1897 0.3413 0.3405 0.2047

0.4462 0.3462 0.2069 0.2253 0.4427 0.2425 0.3300 0.4489 0.2643

Category

Pre = Before liberalization, 1981-84. Post= After liberalization, 1985-88.

74

Indonesia's Financial Liberalization

Finally, it is interesting to discuss the results across industrial sectors. Sector 31 (food and tobacco), sector 32 (textiles and yams), sector 35 (basic chemicals), and sector 38 (machineries, vehicles, and equipments) have the highest profitability both before and after liberalization. Our findings show that indeed they experience an increase in both profit rate and share of investment. On the other hand, sector 33 (wood products) and sector 37 (basic metals) are the sectors experiencing a decline in profit rates and hence their share of investment declines. Somewhat puzzling is the fact that sector 34 (paper products) and sector 36 (cement, ceramics, coal) have decreased their share of investment despite the fact that their profit rates have increased substantially. One plausible explanation for this finding is that there is excess capacity in sector 34 and sector 36, so that a higher profit rate is not followed by an increase in investment. But it is reasonable to conclude that, in general, investment is allocated towards better sectors such as 31, 32, 35, and 38. Taken literally, these results suggest that not only is there an improvement in the allocation of investment towards better categories of firms, but in addition investment goes to better firms within a particular category. One weakness of this type of analysis is that it is not possible to conduct any statistical test of significance. Nevertheless, it gives some insight into the allocation of investment when efficiency is taken into account. For further research, it could be interesting to explore this issue through econometric modelling.

4.5 Conclusions The results obtained from the estimation of different debt equations confirm that the removal of credit ceilings and interest rate controls has indeed affected the allocation of credit in Indonesia. The reform has redirected credit towards more efficient firms, as suggested in financial repression literature. The coefficients of the efficiency index are generally high and significant at the I 0 per cent level. These findings are robust to different measures of efficiency indices derived from different specifications, as well as to different estimation methods. The final regressions, using the two-step regression method, are considered the most appropriate method of estimation. This latter regression supports the conclusions in Chapter 2 that besides firm efficiency, being an exporter has an impact on ability to borrow. It is believed that the policy of export promotion in Indonesia has had a significant effect on efficiency, since these exporting firms subject themselves to competition in the international market. A simple way of calculating two measures of a firm's profitability weighted by its capital stock is used to examine how allocation of investment is affected. It is found that after liberalization, overall profitability increased substantially, suggesting that after liberalization there is an improvement in the allocation of investment. It is also found that investment is reallocated to more efficient firms

4. Efficiency and Credit Allocation

75

in some categories, as well as towards better categories of firms. Export is also the main factor that drives the result. Empirical literature on the link of technical efficiency with allocation of credit and investment in the context of financial reform is still in its infancy, and any conclusions reached thus far must be regarded as tentative. Further research in this area is recommended, which can proceed in either of the following directions. First, this study has focused on technical efficiency. It would be useful to use different measures that take into account, for instance, allocative efficiency. Second, the data does not provide detailed information on types of workers, their educational background, managerial and commercial experience of owners of firms, and so forth. In particular, these factors may have some association with differences in firms' overall performance levels. Finally, it would be interesting to utilize a better econometric method for an investigation into the relationship between efficiency and allocation of investment. Nevertheless, the results are very appealing and help give an insight into the issue of efficiency and allocation of credit and investment. A well-functioning credit market is definitely a prerequisite and conducive to the improvement of the overall economy; the effects in terms of associated increases in efficiency are found in this study. This is even more important especially since this is the first study on the Indonesian manufacturing industry using firm-level data.

NOTES 1. See, among others, Cho (1988), Atiyas (1991), and Jaramillo, Schiantarelli, and Weiss (1993). 2. See Forsund, Lowell, and Schmidt (1980) and Schmidt (1985) for an extensive survey of the literature on frontier production functions and estimation of various types of efficiency. 3. The author has also estimated the same equation by the GLS estimator for random effects, and conducted the Hausman test ofthe random effect against the fixed effect (see Hausman 1978), and the results reject the random effect formulation. Therefore all estimations have been done using the within estimator. 4. See Griliches and Hausman (1986) for a more detailed proof of the argument. Mairesse and Griliches (1990) have also used the Cobb-Douglas function in their study, and argued that using fancier functional forms does not necessarily give better results. Nevertheless, the present author has conducted similar exercises using a trans log production function that yields similar results, which are presented in Appendix II. 5. The same equation has also been estimated using a trans log production function that yields similar results. Only the result of applying a translog form to the last regression method is reported, as shown in Table I of Appendix III. 6. See Table 2 of Appendix III for the results of the same estimation, using efficiency indices obtained from the Instrumental Variables method. In general, the picture remains the same.

76

Indonesia's Financial Liberalization

7. The numerator in Table 4.12 can be simplified (for size category) as [(m/Ks)/s + (1W1/Km)lm + (trl/Kl)Il]. The denominator is [(tr,IK,)(Ks/K)l, + (trm/Km)(Km/K)lm + (tr/K,) (Kl/K)/1]. After some manipulation one can obtain an expression in terms of !size/It and Ksize/Kt; and conclude that the dominance of profit rates of the firm with the largest share of capital will increase the overall denominator, and hence reduce the overall result.

Conclusions and Recommendations

5.1 Conclusions This study investigates the effects of the 1983 financial reform on the financing and investment behaviour of manufacturing establishments in Indonesia during the period 1981-88. The 1983 financial reform was part of an overall structural adjustment programme such as tax reform and trade reform; hence changes in the behaviour of manufacturing establishments may be attributed to other reforms as well as those in the financial sector. However, the study has tried to approach and identify the effects of the financial reform in many different ways. The full sample of 2,970 manufacturing establishments has at least three years of positive output. Consequently, by the nature of the data, new entrants to the market after 1985 have been excluded. Clearly, as suggested by the literature, these young establishments are likely to face financial difficulties that have not been captured. Secondly, the study has calculated descriptive statistics of profitability and financial structure for different categories of firms, both before and after liberalization. Those data suggest that smaller firms benefit disproportionately from enhanced access to external fundings after the reform, despite the increase in interest rates. There is a process of convergence of productivity levels among various categories of establishments, a feature that may suggest increasing economy-wide efficiency. Thirdly, in order to explore analytically the way in which the financial reform may have affected these changes, an appropriate econometric method - the Generalized Method of Moments - is used to capture the dynamic structure of the model and compensate for the presence of general heteroskedasticity across firms over time. Observations with zero-investment level then had to be eliminated, reducing the sample size to 523 establishments. A full explanation of the econometric restrictions is provided in Chapter 3, section 3. However, the analysis of the differences between including and excluding zero-level investment suggests that the findings will be reinforced if

78

Indonesia's Financial Liberalization

an appropriate method is found to employ the full sample. Using appropriate panel data procedures that allow one to control for firmspecific effects, it is identified that after liberalization, smaller firms appear to be less constrained by internally generated funds, and that the degree of leverage as a measure of their degree of deterrence to external finance has been relaxed. This is consistent with theories of capital market segmentation being relaxed after the financial reform. Similar results have been found for larger firms that either do not belong to conglomerate groups or those that are nonexporting. However, for larger firms that are exporting or belong to conglomerate groups, there is evidence that they are not constrained by internal funds. Fourthly, an econometric investigation of the determinants of credit allocation is carried out. This is interesting, since changes in debt reflect the interaction of the demand for credit by firms, particularly given high interest rates, and the behaviour of banks operating in a more competitive (market-based) environment. The study therefore investigates whether the move from administrative-based to market-based allocation of credit increased the flow of credit towards more efficient firms. Once more, for econometric purposes the study is forced to use the same restricted sample used for the investment equation in Chapter 3. The regression results suggest that with the increase in interest rates, more credit was allocated towards more efficient firms after the reform. Moreover, a higher initial degree ofleverage becomes more of a deterrent, and high variability of earnings as a measure of risk also restricts borrowing. These findings confirm the predictions of economic theories, particularly those that have been embodied in financial repression literature. Fifthly, the degree to which efficiency of investment was increased by reform was explored, using a less-structured approach that utilized the full sample. The analysis strongly suggests that investment is concentrated more on efficient firms and on better categories of firms after the reform. Finally, it is seen that different methods, using different amounts of the data, yield consistent conclusions. There is an unavoidable trade-off between level of precision and amount of data covered: increasing precision of quantitative estimates by using more restrictive techniques reduces the amount of information that can be utilized. However, the robustness of the findings suggests that these trade-offs have not been consequential for general conclusions.

5.2 Suggestions for Further Research Empirical literature on the link between financial reform and firms' financing and investment behaviour, as well as between financial reform and efficiency of credit allocation and investment, is still in its infancy. This is the first study that utilizes a huge sample of manufacturing establishments in Indonesia during

5. Conclusions and Recommendations

79

the period 1981-88. Fruitful research in this area can proceed in many directions. Firstly, it would be useful to generate further results using the whole sample as appropriate econometric methods become available. In particular, it would be useful to include the representation of firms with zero-investment levelsit may be that they are facing information problems in the credit market. The second direction for further research is to specify and estimate structural models of investment behaviour that allow a significant role for financing constraints, such as the "Euler Equation Misspecification Approach". Thirdly, with increasing competitiveness in the banking system after 1988, it would be useful to extend the period of study as soon as suitable data become available. In particular, it will be interesting to explore the determinants of new entry, and to investigate whether financial reform has similar effects on new entrance as on the expansion of existing firms. Fourthly, another area of research is to study the substitutability between offshore and domestic finance, as the issue has become more important with the differential increase in domestic real interest rates. Finally, it would be useful to focus also on economic efficiency rather than merely on technical efficiency. Moreover, it is suggested that the Central Bureau of Statistics provide more detailed information on the managerial and educational background of different types of workers and owners, since these factors may have some association with firms' overall performance.

Appendices

APPENDIX 1: DATA CONSTRUCTION This appendix describes the procedure used to construct my data set, including the method used to construct annual capital stocks and stocks of debt that are not available in the original survey data.

1. Data Construction The data has been taken from the annual surveys on manufacturing establishments conducted by the Central Bureau of Statistics since 1975. An additional data set, which proves to be very useful because it contains data on capital stocks and exports, is the 1986 Census of Manufacturing Establishments. The number of establishments in the annual survey varies from 8,300 establishments in 1975 to around 14,000 in 1988, and the 1986 census has 5,830 establishments with complete capital stock data. I select a sample of firms from those two sources as follows. As there is no data available on financial sources prior to 1981, a sample period which runs from 1981 to 1988 is used. The 1981-88 survey data include 4,400 firms with complete data for at least three sequential years of output. The census data include 5,430 firms. Merging the 1981-88 survey with the 1986 census set brings the total number of firms to 3, 192. I then construct a capital stock estimate by back -casting and forecasting the capital stocks, using the capital stock from the 1986 census data as a benchmark (see below for details). Leaving out establishments that have negative and zero capital stock and outliers, and keeping firms that have at least one year of positive investments, leaves us with 2,970 establishments (data set I). The frequency of each category of firms is given in Table 1 of this appendix. This data set is used to calculate the tables in Chapter 2 and the summary statistics in Chapter 4, section 4. A very large number of firms report zero investment for many years. I am unable at this time to determine whether reporting of zero investment is in fact a non-response or represents a real observation of very low investment. I run a logit estimation of investment where it takes a value of 1 if investment is positive, and zero otherwise, the results of which are given in Table 2 of this appendix. The

TABLE 1(a) Frequency of Data for Descriptive Statistics Category

Number of Firms

Per Cent

All sizes

2,970

100.0

Number of years with output positive 3 years 4 years 5 years 6 years 7 years 8 years

156 300 140 198 336 1,840

5.3 10.1 4.7 6.7 11.3 62.0

Firm Size" Small Medium Large

777 1,336 857

26.1 45.0 28.9

Class" Private enterprise Public enterprise

2,505 465

84.3 15.7

Status of ownership' Domestic Foreign/joint venture

2,461 509

82.9 17.1

Group" Non-conglomerate Conglomerate

2,696 274

90.8 9.2

Market' Domestic market Export market

2,369 601

79.8 20.2

689 443 106 251 666 123 33 636 23

23.2 14.9 3.6 8.5 22.4 4.1

Sector 31 Food, beverage, tobacco 32 Textile, yam, leather 33 Wood, furniture, etc. 34 Paper, printing, etc. 35 Chemicals, rubber, plastic, etc. 36 Non-metallic mineral products 37 Basic metals 38 Machineries, equipment, cars 39 Others - toys, etc.

1.1

21.4 0.8

"Small (ml = diag [Zh), (h = 1, ... , H), them orthogonality conditions E(Z'V) = 0 form the basis of the GMM estimator that efficiently exploits all the information in the data. In the presence of general heteroskedasticity across both firms and time, to construct the GMM estimator, U